-
1
-
-
84986522918
-
ICM: A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation
-
Abagyan, R., Totrov, M., and Kuznetsov, D. (1994). ICM: A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 15, 488-506. doi:10.1002/jcc.540150503.
-
(1994)
J. Comput. Chem.
, vol.15
, pp. 488-506
-
-
Abagyan, R.1
Totrov, M.2
Kuznetsov, D.3
-
3
-
-
85026455209
-
Implementing QM in docking calculations: Is it a waste of computational time?
-
Adeniyi, A. A., and Soliman, M. E. S. (2017). Implementing QM in docking calculations: Is it a waste of computational time? Drug Discov. Today 22, 1216-1223. doi:10.1016/j.drudis.2017.06.012.
-
(2017)
Drug Discov. Today
, vol.22
, pp. 1216-1223
-
-
Adeniyi, A.A.1
Soliman, M.E.S.2
-
4
-
-
84945475267
-
Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening: Machine-learning SFs to improve structure-based binding affinity prediction and virtual screening
-
Ain, Q. U., Aleksandrova, A., Roessler, F. D., and Ballester, P. J. (2015). Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening: Machine-learning SFs to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip. Rev. Comput. Mol. Sci. 5, 405-424. doi:10.1002/wcms.1225.
-
(2015)
Wiley Interdiscip. Rev. Comput. Mol. Sci.
, vol.5
, pp. 405-424
-
-
Ain, Q.U.1
Aleksandrova, A.2
Roessler, F.D.3
Ballester, P.J.4
-
5
-
-
84928550315
-
DOCK 6: Impact of new features and current docking performance
-
Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S. R., Moustakas, D. T., Lang, P. T., et al. (2015). DOCK 6: Impact of new features and current docking performance. J. Comput. Chem. 36, 1132-1156. doi:10.1002/jcc.23905.
-
(2015)
J. Comput. Chem.
, vol.36
, pp. 1132-1156
-
-
Allen, W.J.1
Balius, T.E.2
Mukherjee, S.3
Brozell, S.R.4
Moustakas, D.T.5
Lang, P.T.6
-
6
-
-
84948576900
-
Understanding the challenges of protein flexibility in drug design
-
Antunes, D. A., Devaurs, D., and Kavraki, L. E. (2015). Understanding the challenges of protein flexibility in drug design. Exp. Opin. Drug Discov. 10, 1301-1313. doi:10.1517/17460441.2015.1094458.
-
(2015)
Exp. Opin. Drug Discov.
, vol.10
, pp. 1301-1313
-
-
Antunes, D.A.1
Devaurs, D.2
Kavraki, L.E.3
-
7
-
-
42149142447
-
Distance dependent scoring function for describing protein-ligand intermolecular interactions
-
Artemenko, N. (2008). Distance dependent scoring function for describing protein-ligand intermolecular interactions. J. Chem. Inform. Model. 48, 569-574. doi:10.1021/ci700224e.
-
(2008)
J. Chem. Inform. Model.
, vol.48
, pp. 569-574
-
-
Artemenko, N.1
-
8
-
-
84864950736
-
A comparative assessment of ranking accuracies of conventional and machine-learning-based scoring functions for protein-ligand binding affinity prediction
-
Ashtawy, H. M., and Mahapatra, N. R. (2012). A comparative assessment of ranking accuracies of conventional and machine-learning-based scoring functions for protein-ligand binding affinity prediction. IEEEACM Trans. Comput. Biol. Bioinforma. IEEE ACM 9, 1301-1313. doi:10.1109/TCBB.2012.36.
-
(2012)
IEEEACM Trans. Comput. Biol. Bioinforma. IEEE ACM
, vol.9
, pp. 1301-1313
-
-
Ashtawy, H.M.1
Mahapatra, N.R.2
-
9
-
-
84977574223
-
BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes
-
Ashtawy, H. M., and Mahapatra, N. R. (2015). BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes. BMC Bioinformatics 16(Suppl. 4):S8. doi:10.1186/1471-2105-16-S4-S8.
-
(2015)
BMC Bioinformatics
, vol.16
, pp. S8
-
-
Ashtawy, H.M.1
Mahapatra, N.R.2
-
10
-
-
85040924553
-
Task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment
-
Ashtawy, H. M., and Mahapatra, N. R. (2018). Task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment. J. Chem. Inform. Model. 58, 119-133. doi:10.1021/acs.jcim.7b00309.
-
(2018)
J. Chem. Inform. Model.
, vol.58
, pp. 119-133
-
-
Ashtawy, H.M.1
Mahapatra, N.R.2
-
11
-
-
85020136111
-
Modeling covalent-modifier drugs
-
Awoonor-Williams, E., Walsh, A. G., and Rowley, C. N. (2017). Modeling covalent-modifier drugs. Biochim. Biophys. Acta BBA - Proteins Proteom. 1865, 1664-1675. doi:10.1016/j.bbapap.2017.05.009.
-
(2017)
Biochim. Biophys. Acta BBA - Proteins Proteom
, vol.1865
, pp. 1664-1675
-
-
Awoonor-Williams, E.1
Walsh, A.G.2
Rowley, C.N.3
-
12
-
-
85020550984
-
GalaxyDock BP2 score: A hybrid scoring function for accurate protein-ligand docking
-
Baek, M., Shin, W.-H., Chung, H. W., and Seok, C. (2017). GalaxyDock BP2 score: A hybrid scoring function for accurate protein-ligand docking. J. Comput. Aided Mol. Des. 31, 653-666. doi:10.1007/s10822-017-0030-9.
-
(2017)
J. Comput. Aided Mol. Des.
, vol.31
, pp. 653-666
-
-
Baek, M.1
Shin, W.-H.2
Chung, H.W.3
Seok, C.4
-
13
-
-
0034604105
-
A surprising simplicity to protein folding
-
Baker, D. (2000). A surprising simplicity to protein folding. Nature 405, 39-42. doi:10.1038/35011000.
-
(2000)
Nature
, vol.405
, pp. 39-42
-
-
Baker, D.1
-
14
-
-
77952825581
-
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking
-
Ballester, P. J., and Mitchell, J. B. O. (2010). A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26, 1169-1175. doi:10.1093/bioinformatics/btq112.
-
(2010)
Bioinformatics
, vol.26
, pp. 1169-1175
-
-
Ballester, P.J.1
Mitchell, J.B.O.2
-
15
-
-
80051984855
-
Comments on "leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets": Significance for the validation of scoring functions
-
Ballester, P. J., and Mitchell, J. B. O. (2011). Comments on "leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets": Significance for the validation of scoring functions. J. Chem. Inform. Model. 51, 1739-1741. doi:10.1021/ci200057e.
-
(2011)
J. Chem. Inform. Model.
, vol.51
, pp. 1739-1741
-
-
Ballester, P.J.1
Mitchell, J.B.O.2
-
16
-
-
85020478839
-
Best practices of computer-aided drug discovery: Lessons learned from the development of a preclinical candidate for prostate cancer with a new mechanism of action
-
Ban, F., Dalal, K., Li, H., LeBlanc, E., Rennie, P. S., and Cherkasov, A. (2017). Best practices of computer-aided drug discovery: Lessons learned from the development of a preclinical candidate for prostate cancer with a new mechanism of action. J. Chem. Inform. Model. 57, 1018-1028. doi:10.1021/acs.jcim.7b00137.
-
(2017)
J. Chem. Inform. Model.
, vol.57
, pp. 1018-1028
-
-
Ban, F.1
Dalal, K.2
Li, H.3
Le Blanc, E.4
Rennie, P.S.5
Cherkasov, A.6
-
17
-
-
65249141152
-
SeleX-CS: A new consensus scoring algorithm for hit discovery and lead optimization
-
Bar-Haim, S., Aharon, A., Ben-Moshe, T., Marantz, Y., and Senderowitz, H. (2009). SeleX-CS: A new consensus scoring algorithm for hit discovery and lead optimization. J. Chem. Inform. Model. 49, 623-633. doi:10.1021/ci800335j.
-
(2009)
J. Chem. Inform. Model.
, vol.49
, pp. 623-633
-
-
Bar-Haim, S.1
Aharon, A.2
Ben-Moshe, T.3
Marantz, Y.4
Senderowitz, H.5
-
18
-
-
85026890626
-
Computer-aided drug design: Time to play with novel chemical matter
-
Barril, X. (2017). Computer-aided drug design: Time to play with novel chemical matter. Expert Opin. Drug Discov. 12, 977-980. doi:10.1080/17460441.2017.1362386.
-
(2017)
Expert Opin. Drug Discov.
, vol.12
, pp. 977-980
-
-
Barril, X.1
-
19
-
-
84879583689
-
Evaluation and optimization of virtual screening workflows with DEKOIS 2.0 - A public library of challenging docking benchmark sets
-
Bauer, M. R., Ibrahim, T. M., Vogel, S. M., and Boeckler, F. M. (2013). Evaluation and optimization of virtual screening workflows with DEKOIS 2.0 - A public library of challenging docking benchmark sets. J. Chem. Inform. Model. 53, 1447-1462. doi:10.1021/ci400115b.
-
(2013)
J. Chem. Inform. Model.
, vol.53
, pp. 1447-1462
-
-
Bauer, M.R.1
Ibrahim, T.M.2
Vogel, S.M.3
Boeckler, F.M.4
-
20
-
-
77950022453
-
Non-additivity of functional group contributions in protein-ligand binding: A comprehensive study by crystallography and isothermal titration calorimetry
-
Baum, B., Muley, L., Smolinski, M., Heine, A., Hangauer, D., and Klebe, G. (2010). Non-additivity of functional group contributions in protein-ligand binding: A comprehensive study by crystallography and isothermal titration calorimetry. J. Mol. Biol. 397, 1042-1054. doi:10.1016/j.jmb.2010.02.007.
-
(2010)
J. Mol. Biol.
, vol.397
, pp. 1042-1054
-
-
Baum, B.1
Muley, L.2
Smolinski, M.3
Heine, A.4
Hangauer, D.5
Klebe, G.6
-
21
-
-
85033491221
-
Lessons learned in induced fit docking and metadynamics in the drug design data resource grand challenge 2
-
Baumgartner, M. P., and Evans, D. A. (2018). Lessons learned in induced fit docking and metadynamics in the drug design data resource grand challenge 2. J. Comput. Aided Mol. Des. 32, 45-58. doi:10.1007/s10822-017-0081-y.
-
(2018)
J. Comput. Aided Mol. Des.
, vol.32
, pp. 45-58
-
-
Baumgartner, M.P.1
Evans, D.A.2
-
22
-
-
0033954256
-
The protein data bank
-
Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., et al. (2000). The protein data bank. Nucleic Acids Res. 28, 235-242.
-
(2000)
Nucleic Acids Res.
, vol.28
, pp. 235-242
-
-
Berman, H.M.1
Westbrook, J.2
Feng, Z.3
Gilliland, G.4
Bhat, T.N.5
Weissig, H.6
-
23
-
-
33746867935
-
GFscore: A general nonlinear consensus scoring function for high-throughput docking
-
Betzi, S., Suhre, K., Chétrit, B., Guerlesquin, F., and Morelli, X. (2006). GFscore: A general nonlinear consensus scoring function for high-throughput docking. J. Chem. Inform. Model. 46, 1704-1712. doi:10.1021/ci0600758.
-
(2006)
J. Chem. Inform. Model.
, vol.46
, pp. 1704-1712
-
-
Betzi, S.1
Suhre, K.2
Chétrit, B.3
Guerlesquin, F.4
Morelli, X.5
-
24
-
-
84959230770
-
Covalent docking using autodock: Two-point attractor and flexible side chain methods
-
Bianco, G., Forli, S., Goodsell, D. S., and Olson, A. J. (2016). Covalent docking using autodock: Two-point attractor and flexible side chain methods. Protein Sci. Publ. Protein Soc. 25, 295-301doi:10.1002/pro.2733.
-
(2016)
Protein Sci. Publ. Protein Soc.
, vol.25
, pp. 295-301
-
-
Bianco, G.1
Forli, S.2
Goodsell, D.S.3
Olson, A.J.4
-
25
-
-
84873690392
-
Sparse projected-gradient method as a linear-scaling low-memory alternative to diagonalization in self-consistent field electronic structure calculations
-
Birgin, E. G., Martiìnez, J. M., Martiìnez, L., and Rocha, G. B. (2013). Sparse projected-gradient method as a linear-scaling low-memory alternative to diagonalization in self-consistent field electronic structure calculations. J. Chem. Theory Comput. 9, 1043-1051. doi:10.1021/ct3009683.
-
(2013)
J. Chem. Theory Comput.
, vol.9
, pp. 1043-1051
-
-
Birgin, E.G.1
Martiìnez, J.M.2
Martiìnez, L.3
Rocha, G.B.4
-
27
-
-
0034649618
-
Protein-based virtual screening of chemical databases. 1, evaluation of different docking/scoring combinations
-
Bissantz, C., Folkers, G., and Rognan, D. (2000). Protein-based virtual screening of chemical databases. 1, evaluation of different docking/scoring combinations. J. Med. Chem. 43, 4759-4767.
-
(2000)
J. Med. Chem.
, vol.43
, pp. 4759-4767
-
-
Bissantz, C.1
Folkers, G.2
Rognan, D.3
-
28
-
-
0027485997
-
Energetic cost and structural consequences of burying a hydroxyl group within the core of a protein determined from Ala.fwdarw, ser and Val.fwdarw. Thr substitutions in T4 lysozyme
-
Mosc
-
Blaber, M., Lindstrom, J. D., Gassner, N., Xu, J., Heinz, D. W., and Matthews, B. W. (1993). Energetic cost and structural consequences of burying a hydroxyl group within the core of a protein determined from Ala.fwdarw, ser and Val.fwdarw. Thr substitutions in T4 lysozyme. Biochemistry (Mosc.) 32, 11363-11373. doi:10.1021/bi00093a013.
-
(1993)
Biochemistry
, vol.32
, pp. 11363-11373
-
-
Blaber, M.1
Lindstrom, J.D.2
Gassner, N.3
Xu, J.4
Heinz, D.W.5
Matthews, B.W.6
-
29
-
-
84969931994
-
Water, water, everywhere It's time to stop and think
-
Bodnarchuk, M. S. (2016). Water, water, everywhere It's time to stop and think. Drug Discov. Today 21, 1139-1146. doi:10.1016/j.drudis.2016.05.009.
-
(2016)
Drug Discov. Today
, vol.21
, pp. 1139-1146
-
-
Bodnarchuk, M.S.1
-
30
-
-
0026813925
-
The computer program LUDI: A new method for the de novo design of enzyme inhibitors
-
Böhm, H. J. (1992). The computer program LUDI: A new method for the de novo design of enzyme inhibitors. J. Comput. Aided Mol. Des. 6, 61-78.
-
(1992)
J. Comput. Aided Mol. Des.
, vol.6
, pp. 61-78
-
-
Böhm, H.J.1
-
31
-
-
0028454828
-
The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure
-
Böhm, H. J. (1994). The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aided Mol. Des. 8, 243-256.
-
(1994)
J. Comput. Aided Mol. Des.
, vol.8
, pp. 243-256
-
-
Böhm, H.J.1
-
32
-
-
84884429366
-
Successful applications of in silico approaches for lead/drug discovery
-
ed. M. A. Miteva Emirate of Sharjah: Bentham Science Publishers
-
Bortolato, A., Perruccio, F., and Moro, S. (2012). "successful applications of in silico approaches for lead/drug discovery, " in In-Silico Lead Discovery, ed. M. A. Miteva (Emirate of Sharjah: Bentham Science Publishers), 163-175.
-
(2012)
In-Silico Lead Discovery
, pp. 163-175
-
-
Bortolato, A.1
Perruccio, F.2
Moro, S.3
-
33
-
-
79956201474
-
Systematic exploitation of multiple receptor conformations for virtual ligand screening
-
Bottegoni, G., Rocchia, W., Rueda, M., Abagyan, R., and Cavalli, A. (2011). Systematic exploitation of multiple receptor conformations for virtual ligand screening. PLoS One 6:e18845. doi:10.1371/journal.pone.0018845.
-
(2011)
PLoS One
, vol.6
, pp. e18845
-
-
Bottegoni, G.1
Rocchia, W.2
Rueda, M.3
Abagyan, R.4
Cavalli, A.5
-
34
-
-
85044409738
-
Shedding light on important waters for drug design: Simulations versus grid-based methods
-
Bucher, D., Stouten, P., and Triballeau, N. (2018). Shedding light on important waters for drug design: Simulations versus grid-based methods. J. Chem. Inform. Model. 58, 692-699. doi:10.1021/acs.jcim.7b00642.
-
(2018)
J. Chem. Inform. Model.
, vol.58
, pp. 692-699
-
-
Bucher, D.1
Stouten, P.2
Triballeau, N.3
-
35
-
-
84933519816
-
Protein flexibility in drug discovery: From theory to computation
-
Buonfiglio, R., Recanatini, M., and Masetti, M. (2015). Protein flexibility in drug discovery: From theory to computation. Chem Med Chem 10, 1141-1148. doi:10.1002/cmdc.201500086.
-
(2015)
Chem Med Chem
, vol.10
, pp. 1141-1148
-
-
Buonfiglio, R.1
Recanatini, M.2
Masetti, M.3
-
36
-
-
84902438255
-
Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model
-
Cao, Y., and Li, L. (2014). Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model. Bioinform. Oxf. Engl. 30, 1674-1680. doi:10.1093/bioinformatics/btu104.
-
(2014)
Bioinform. Oxf. Engl.
, vol.30
, pp. 1674-1680
-
-
Cao, Y.1
Li, L.2
-
37
-
-
84883260321
-
Check your confidence: Size really does matter
-
Carlson, H. A. (2013). Check your confidence: Size really does matter. J. Chem. Inform. Model. 53, 1837-1841. doi:10.1021/ci4004249.
-
(2013)
J. Chem. Inform. Model.
, vol.53
, pp. 1837-1841
-
-
Carlson, H.A.1
-
38
-
-
84976501156
-
Lessons learned over four benchmark exercises from the community structure-activity resource
-
Carlson, H. A. (2016). Lessons learned over four benchmark exercises from the community structure-activity resource. J. Chem. Inform. Model. 56, 951-954. doi:10.1021/acs.jcim.6b00182.
-
(2016)
J. Chem. Inform. Model.
, vol.56
, pp. 951-954
-
-
Carlson, H.A.1
-
39
-
-
54749115273
-
Docking and high throughput docking: Successes and the challenge of protein flexibility
-
Cavasotto, C., and Singh, N. (2008). Docking and high throughput docking: Successes and the challenge of protein flexibility. Curr. Comput. Aided-Drug Des. 4, 221-234. doi:10.2174/157340908785747474.
-
(2008)
Curr. Comput. Aided-Drug Des.
, vol.4
, pp. 221-234
-
-
Cavasotto, C.1
Singh, N.2
-
40
-
-
85048220166
-
Quantum chemical approaches in structure-based virtual screening and lead optimization
-
Cavasotto, C. N., Adler, N. S., and Aucar, M. G. (2018). Quantum chemical approaches in structure-based virtual screening and lead optimization. Front. Chem. 6:188. doi:10.3389/fchem.2018.00188.
-
(2018)
Front. Chem.
, vol.6
, pp. 188
-
-
Cavasotto, C.N.1
Adler, N.S.2
Aucar, M.G.3
-
41
-
-
33846822002
-
Ligand configurational entropy and protein binding
-
Chang, C. A., Chen, W., and Gilson, M. K. (2007). Ligand configurational entropy and protein binding. Proc. Natl. Acad. Sci. U.S.A. 104, 1534-1539. doi:10.1073/pnas.0610494104.
-
(2007)
Proc. Natl. Acad. Sci. U.S.A.
, vol.104
, pp. 1534-1539
-
-
Chang, C.A.1
Chen, W.2
Gilson, M.K.3
-
42
-
-
84960494445
-
VSDC: A method to improve early recognition in virtual screening when limited experimental resources are available
-
Chaput, L., Martinez-Sanz, J., Quiniou, E., Rigolet, P., Saettel, N., and Mouawad, L. (2016a). vSDC: A method to improve early recognition in virtual screening when limited experimental resources are available. J. Cheminformatics 8:1. doi:10.1186/s13321-016-0112-z.
-
(2016)
J. Cheminformatics
, vol.8
, pp. 1
-
-
Chaput, L.1
Martinez-Sanz, J.2
Quiniou, E.3
Rigolet, P.4
Saettel, N.5
Mouawad, L.6
-
43
-
-
84995505949
-
Benchmark of four popular virtual screening programs: Construction of the active/decoy dataset remains a major determinant of measured performance
-
Chaput, L., Martinez-Sanz, J., Saettel, N., and Mouawad, L. (2016b). Benchmark of four popular virtual screening programs: Construction of the active/decoy dataset remains a major determinant of measured performance. J. Cheminformatics 8:56. doi:10.1186/s13321-016-0167-x.
-
(2016)
J. Cheminformatics
, vol.8
, pp. 56
-
-
Chaput, L.1
Martinez-Sanz, J.2
Saettel, N.3
Mouawad, L.4
-
44
-
-
85020469917
-
Efficient conformational sampling and weak scoring in docking programs? Strategy of the wisdom of crowds
-
Chaput, L., and Mouawad, L. (2017). Efficient conformational sampling and weak scoring in docking programs? Strategy of the wisdom of crowds. J. Cheminformatics 9:37. doi:10.1186/s13321-017-0227-x.
-
(2017)
J. Cheminformatics
, vol.9
, pp. 37
-
-
Chaput, L.1
Mouawad, L.2
-
45
-
-
0033576680
-
Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins
-
Charifson, P. S., Corkery, J. J., Murcko, M. A., and Walters, W. P. (1999). Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem. 42, 5100-5109. doi:10.1021/jm990352k.
-
(1999)
J. Med. Chem
, vol.42
, pp. 5100-5109
-
-
Charifson, P.S.1
Corkery, J.J.2
Murcko, M.A.3
Walters, W.P.4
-
46
-
-
85014022040
-
On-the-fly QM/MM docking with attracting cavities
-
Chaskar, P., Zoete, V., and Röhrig, U. F. (2017). On-the-fly QM/MM docking with attracting cavities. J. Chem. Inform. Model. 57, 73-84. doi:10.1021/acs.jcim.6b00406.
-
(2017)
J. Chem. Inform. Model.
, vol.57
, pp. 73-84
-
-
Chaskar, P.1
Zoete, V.2
Röhrig, U.F.3
-
47
-
-
66149103553
-
Comparative assessment of scoring functions on a diverse test set
-
Cheng, T., Li, X., Li, Y., Liu, Z., and Wang, R. (2009). Comparative assessment of scoring functions on a diverse test set. J. Chem. Inform. Model. 49, 1079-1093. doi:10.1021/ci9000053.
-
(2009)
J. Chem. Inform. Model.
, vol.49
, pp. 1079-1093
-
-
Cheng, T.1
Li, X.2
Li, Y.3
Liu, Z.4
Wang, R.5
-
48
-
-
84969622308
-
Semiempirical quantum mechanical methods for noncovalent interactions for chemical and biochemical applications
-
Christensen, A. S., Kubaø, T., Cui, Q., and Elstner, M. (2016). Semiempirical quantum mechanical methods for noncovalent interactions for chemical and biochemical applications. Chem. Rev. 116, 5301-5337. doi:10.1021/acs.chemrev.5b00584.
-
(2016)
Chem. Rev.
, vol.116
, pp. 5301-5337
-
-
Christensen, A.S.1
Kuba, T.2
Cui, Q.3
Elstner, M.4
-
49
-
-
85019543800
-
The basis for target-based virtual screening: Protein structures
-
ed. C. Sotriffer Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA
-
Cole, J. C., Korb, O., Olsson, T. S. G., and Liebeschuetz, J. (2011). "The basis for target-based virtual screening: Protein structures, " in Methods and Principles in Medicinal Chemistry, ed. C. Sotriffer (Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA), 87-114. doi:10.1002/9783527633326.ch4.
-
(2011)
Methods and Principles in Medicinal Chemistry
, pp. 87-114
-
-
Cole, J.C.1
Korb, O.2
Olsson, T.S.G.3
Liebeschuetz, J.4
-
50
-
-
84865271039
-
Variability in docking success rates due to dataset preparation
-
Corbeil, C. R., Williams, C. I., and Labute, P. (2012). Variability in docking success rates due to dataset preparation. J. Comput. Aided Mol. Des. 26, 775-786. doi:10.1007/s10822-012-9570-1.
-
(2012)
J. Comput. Aided Mol. Des.
, vol.26
, pp. 775-786
-
-
Corbeil, C.R.1
Williams, C.I.2
Labute, P.3
-
51
-
-
85023759573
-
Paying the Price of desolvation in solvent-exposed protein pockets: Impact of distal solubilizing groups on affinity and binding thermodynamics in a series of thermolysin inhibitors
-
Cramer, J., Krimmer, S. G., Heine, A., and Klebe, G. (2017). Paying the Price of desolvation in solvent-exposed protein pockets: Impact of distal solubilizing groups on affinity and binding thermodynamics in a series of thermolysin inhibitors. J. Med. Chem. 60, 5791-5799. doi:10.1021/acs.jmedchem.7b00490.
-
(2017)
J. Med. Chem.
, vol.60
, pp. 5791-5799
-
-
Cramer, J.1
Krimmer, S.G.2
Heine, A.3
Klebe, G.4
-
52
-
-
85028956021
-
Quantum-mechanics methodologies in drug discovery: Applications of docking and scoring in lead optimization
-
Crespo, A., Rodriguez-Granillo, A., and Lim, V. T. (2017). Quantum-mechanics methodologies in drug discovery: Applications of docking and scoring in lead optimization. Curr. Top. Med. Chem. 17, 2663-2680. doi:10.2174/1568026617666170707120609.
-
(2017)
Curr. Top. Med. Chem.
, vol.17
, pp. 2663-2680
-
-
Crespo, A.1
Rodriguez-Granillo, A.2
Lim, V.T.3
-
53
-
-
84883209345
-
CSAR benchmark exercise 2011-2012: Evaluation of results from docking and relative ranking of blinded congeneric series
-
Damm-Ganamet, K. L., Smith, R. D., Dunbar, J. B., Stuckey, J. A., and Carlson, H. A. (2013). CSAR benchmark exercise 2011-2012: Evaluation of results from docking and relative ranking of blinded congeneric series. J. Chem. Inform. Model. 53, 1853-1870. doi:10.1021/ci400025f.
-
(2013)
J. Chem. Inform. Model.
, vol.53
, pp. 1853-1870
-
-
Damm-Ganamet, K.L.1
Smith, R.D.2
Dunbar, J.B.3
Stuckey, J.A.4
Carlson, H.A.5
-
54
-
-
84927746174
-
Structure based virtual screening to discover putative drug candidates: Necessary considerations and successful case studies
-
Danishuddin, M., and Khan, A. U. (2015). Structure based virtual screening to discover putative drug candidates: Necessary considerations and successful case studies. Methods 71, 135-145. doi:10.1016/j.ymeth.2014.10.019.
-
(2015)
Methods
, vol.71
, pp. 135-145
-
-
Danishuddin, M.1
Khan, A.U.2
-
55
-
-
85021168826
-
Covalent inhibitors design and discovery
-
De Cesco, S., Kurian, J., Dufresne, C., Mittermaier, A. K., and Moitessier, N. (2017). Covalent inhibitors design and discovery. Eur. J. Med. Chem. 138, 96-114. doi:10.1016/j.ejmech.2017.06.019.
-
(2017)
Eur. J. Med. Chem.
, vol.138
, pp. 96-114
-
-
De Cesco, S.1
Kurian, J.2
Dufresne, C.3
Mittermaier, A.K.4
Moitessier, N.5
-
56
-
-
84948614446
-
A dynamic niching genetic algorithm strategy for docking highly flexible ligands
-
de Magalhães, C. S., Almeida, D. M., Barbosa, H. J. C., and Dardenne, L. E. (2014). A dynamic niching genetic algorithm strategy for docking highly flexible ligands. Inform. Sci. 289, 206-224. doi:10.1016/j.ins.2014.08.002.
-
(2014)
Inform. Sci.
, vol.289
, pp. 206-224
-
-
De Magalhães, C.S.1
Almeida, D.M.2
Barbosa, H.J.C.3
Dardenne, L.E.4
-
57
-
-
85025107228
-
A hybrid knowledge-based and empirical scoring function for protein-ligand interaction: SMoG2016
-
Debroise, T., Shakhnovich, E. I., and Chéron, N. (2017). A hybrid knowledge-based and empirical scoring function for protein-ligand interaction: SMoG2016. J. Chem. Inform. Model. 57, 584-593. doi:10.1021/acs.jcim.6b00610.
-
(2017)
J. Chem. Inform. Model.
, vol.57
, pp. 584-593
-
-
Debroise, T.1
Shakhnovich, E.I.2
Chéron, N.3
-
58
-
-
84881059458
-
Definition of the halogen bond (IUPAC Recommendations 2013)
-
Desiraju, G. R., Ho, P. S., Kloo, L., Legon, A. C., Marquardt, R., Metrangolo, P., et al. (2013). Definition of the halogen bond (IUPAC Recommendations 2013). Pure Appl. Chem. 85, 1711-1713. doi:10.1351/PAC-REC-12-05-10.
-
(2013)
Pure Appl. Chem.
, vol.85
, pp. 1711-1713
-
-
Desiraju, G.R.1
Ho, P.S.2
Kloo, L.3
Legon, A.C.4
Marquardt, R.5
Metrangolo, P.6
-
59
-
-
0031022887
-
Additivity principles in biochemistry
-
Dill, K. A. (1997). Additivity principles in biochemistry. J. Biol. Chem. 272, 701-704. doi:10.1074/jbc.272.2.701.
-
(1997)
J. Biol. Chem.
, vol.272
, pp. 701-704
-
-
Dill, K.A.1
-
60
-
-
84959128138
-
Innovation in the pharmaceutical industry: New estimates of R&D costs
-
DiMasi, J. A., Grabowski, H. G., and Hansen, R. W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. J. Health Econ. 47, 20-33. doi:10.1016/j.jhealeco.2016.01.012.
-
(2016)
J. Health Econ.
, vol.47
, pp. 20-33
-
-
Di Masi, J.A.1
Grabowski, H.G.2
Hansen, R.W.3
-
61
-
-
84873041650
-
Characterization of small molecule binding. I. Accurate Identification of Strong Inhibitors in Virtual Screening
-
Ding, B., Wang, J., Li, N., and Wang, W. (2013). Characterization of small molecule binding. I. Accurate Identification of Strong Inhibitors in Virtual Screening. J. Chem. Inform. Model. 53, 114-122. doi:10.1021/ci300508m.
-
(2013)
J. Chem. Inform. Model.
, vol.53
, pp. 114-122
-
-
Ding, B.1
Wang, J.2
Li, N.3
Wang, W.4
-
62
-
-
0346506503
-
Fast, accurate semiempirical molecular orbital calculations for macromolecules
-
Dixon, S. L., and Merz, K. M. (1997). Fast, accurate semiempirical molecular orbital calculations for macromolecules. J. Chem. Phys. 107, 879-893. doi:10.1063/1.474386.
-
(1997)
J. Chem. Phys.
, vol.107
, pp. 879-893
-
-
Dixon, S.L.1
Merz, K.M.2
-
63
-
-
85044826322
-
Practices in molecular docking and structure-based virtual screening
-
Dos Santos, R. N., Ferreira, L. G., and Andricopulo, A. D. (2018). Practices in molecular docking and structure-based virtual screening. Methods Mol. Biol. Clifton NJ 1762, 31-50. doi:10.1007/978-1-4939-7756-7-3.
-
(2018)
Methods Mol. Biol. Clifton NJ 1762
, pp. 31-50
-
-
Dos Santos, R.N.1
Ferreira, L.G.2
Andricopulo, A.D.3
-
64
-
-
0031226772
-
Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes
-
Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V., and Mee, R. P. (1997). Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput. Aided Mol. Des. 11, 425-445.
-
(1997)
J. Comput. Aided Mol. Des.
, vol.11
, pp. 425-445
-
-
Eldridge, M.D.1
Murray, C.W.2
Auton, T.R.3
Paolini, G.V.4
Mee, R.P.5
-
65
-
-
41349090342
-
Can we use docking and scoring for hit-to-lead optimization?
-
Enyedy, I. J., and Egan, W. J. (2008). Can we use docking and scoring for hit-to-lead optimization? J. Comput. Aided Mol. Des. 22, 161-168. doi:10.1007/s10822-007-9165-4.
-
(2008)
J. Comput. Aided Mol. Des.
, vol.22
, pp. 161-168
-
-
Enyedy, I.J.1
Egan, W.J.2
-
66
-
-
85025668923
-
Machine learning consensus scoring improves performance across targets in structure-based virtual screening
-
Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., et al. (2017). Machine learning consensus scoring improves performance across targets in structure-based virtual screening. J. Chem. Inform. Model. 57, 1579-1590. doi:10.1021/acs.jcim.7b00153.
-
(2017)
J. Chem. Inform. Model.
, vol.57
, pp. 1579-1590
-
-
Ericksen, S.S.1
Wu, H.2
Zhang, H.3
Michael, L.A.4
Newton, M.A.5
Hoffmann, F.M.6
-
67
-
-
1542390499
-
Identification of protein-protein interaction sites from docking energy landscapes
-
Fernández-Recio, J., Totrov, M., and Abagyan, R. (2004). Identification of protein-protein interaction sites from docking energy landscapes. J. Mol. Biol. 335, 843-865.
-
(2004)
J. Mol. Biol.
, vol.335
, pp. 843-865
-
-
Fernández-Recio, J.1
Totrov, M.2
Abagyan, R.3
-
68
-
-
4744365803
-
Soft docking and multiple receptor conformations in virtual screening
-
Ferrari, A. M., Wei, B. Q., Costantino, L., and Shoichet, B. K. (2004). Soft docking and multiple receptor conformations in virtual screening. J. Med. Chem. 47, 5076-5084. doi:10.1021/jm049756p.
-
(2004)
J. Med. Chem.
, vol.47
, pp. 5076-5084
-
-
Ferrari, A.M.1
Wei, B.Q.2
Costantino, L.3
Shoichet, B.K.4
-
69
-
-
84938316204
-
Molecular docking and structure-based drug design strategies
-
Ferreira, L., dos Santos, R., Oliva, G., and Andricopulo, A. (2015). Molecular docking and structure-based drug design strategies. Molecules 20, 13384-13421. doi:10.3390/molecules200713384.
-
(2015)
Molecules
, vol.20
, pp. 13384-13421
-
-
Ferreira, L.1
Dos Santos, R.2
Oliva, G.3
Andricopulo, A.4
-
70
-
-
69049101145
-
Divergent modes of enzyme inhibition in a homologous structure-activity series
-
Ferreira, R. S., Bryant, C., Ang, K. K. H., McKerrow, J. H., Shoichet, B. K., and Renslo, A. R. (2009). Divergent modes of enzyme inhibition in a homologous structure-activity series. J. Med. Chem. 52, 5005-5008. doi:10.1021/jm9009229.
-
(2009)
J. Med. Chem.
, vol.52
, pp. 5005-5008
-
-
Ferreira, R.S.1
Bryant, C.2
Ang, K.K.H.3
McKerrow, J.H.4
Shoichet, B.K.5
Renslo, A.R.6
-
71
-
-
84903133311
-
Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery
-
Fischer, M., Coleman, R. G., Fraser, J. S., and Shoichet, B. K. (2014). Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat. Chem. 6, 575-583. doi:10.1038/nchem.1954.
-
(2014)
Nat. Chem.
, vol.6
, pp. 575-583
-
-
Fischer, M.1
Coleman, R.G.2
Fraser, J.S.3
Shoichet, B.K.4
-
72
-
-
84960866293
-
Computational tools to model halogen bonds in medicinal chemistry
-
Ford, M. C., and Ho, P. S. (2016). Computational tools to model halogen bonds in medicinal chemistry. J. Med. Chem. 59, 1655-1670. doi:10.1021/acs.jmedchem.5b00997.
-
(2016)
J. Med. Chem.
, vol.59
, pp. 1655-1670
-
-
Ford, M.C.1
Ho, P.S.2
-
73
-
-
84856397848
-
A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking
-
Forli, S., and Olson, A. J. (2012). A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking. J. Med. Chem. 55, 623-638. doi:10.1021/jm2005145.
-
(2012)
J. Med. Chem.
, vol.55
, pp. 623-638
-
-
Forli, S.1
Olson, A.J.2
-
74
-
-
52049123291
-
Do enthalpy and entropy distinguish first in class from best in class?
-
Freire, E. (2008). Do enthalpy and entropy distinguish first in class from best in class? Drug Discov. Today 13, 869-874. doi:10.1016/j.drudis.2008.07.005.
-
(2008)
Drug Discov. Today
, vol.13
, pp. 869-874
-
-
Freire, E.1
-
75
-
-
12144289984
-
Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy
-
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., et al. (2004). Glide: A new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy. J. Med. Chem. 47, 1739-1749. doi:10.1021/jm0306430.
-
(2004)
J. Med. Chem.
, vol.47
, pp. 1739-1749
-
-
Friesner, R.A.1
Banks, J.L.2
Murphy, R.B.3
Halgren, T.A.4
Klicic, J.J.5
Mainz, D.T.6
-
76
-
-
33750124980
-
Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes
-
Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., et al. (2006a). Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem. 49, 6177-6196. doi:10.1021/jm051256o.
-
(2006)
J. Med. Chem.
, vol.49
, pp. 6177-6196
-
-
Friesner, R.A.1
Murphy, R.B.2
Repasky, M.P.3
Frye, L.L.4
Greenwood, J.R.5
Halgren, T.A.6
-
77
-
-
33750124980
-
Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes
-
Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., et al. (2006b). Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem. 49, 6177-6196. doi:10.1021/jm051256o.
-
(2006)
J. Med. Chem.
, vol.49
, pp. 6177-6196
-
-
Friesner, R.A.1
Murphy, R.B.2
Repasky, M.P.3
Frye, L.L.4
Greenwood, J.R.5
Halgren, T.A.6
-
78
-
-
84908242076
-
Beware of machine learning-based scoring functions-on the danger of developing black boxes
-
Gabel, J., Desaphy, J., and Rognan, D. (2014). Beware of machine learning-based scoring functions-on the danger of developing black boxes. J. Chem. Inform. Model. 54, 2807-2815. doi:10.1021/ci500406k.
-
(2014)
J. Chem. Inform. Model.
, vol.54
, pp. 2807-2815
-
-
Gabel, J.1
Desaphy, J.2
Rognan, D.3
-
79
-
-
85036546727
-
D3R grand challenge 2: Blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies
-
Gaieb, Z., Liu, S., Gathiaka, S., Chiu, M., Yang, H., Shao, C., et al. (2018). D3R grand challenge 2: Blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J. Comput. Aided Mol. Des. 32, 1-20. doi:10.1007/s10822-017-0088-4.
-
(2018)
J. Comput. Aided Mol. Des.
, vol.32
, pp. 1-20
-
-
Gaieb, Z.1
Liu, S.2
Gathiaka, S.3
Chiu, M.4
Yang, H.5
Shao, C.6
-
80
-
-
85030699657
-
Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: Lessons learned from a collaborative effort
-
Gao, Y.-D., Hu, Y., Crespo, A., Wang, D., Armacost, K. A., Fells, J. I., et al. (2018). Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: Lessons learned from a collaborative effort. J. Comput. Aided Mol. Des. 32, 129-142. doi:10.1007/s10822-017-0072-z.
-
(2018)
J. Comput. Aided Mol. Des.
, vol.32
, pp. 129-142
-
-
Gao, Y.-D.1
Hu, Y.2
Crespo, A.3
Wang, D.4
Armacost, K.A.5
Fells, J.I.6
-
81
-
-
84989159600
-
D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions
-
Gathiaka, S., Liu, S., Chiu, M., Yang, H., Stuckey, J. A., Kang, Y. N., et al. (2016). D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions. J. Comput. Aided Mol. Des. 30, 651-668. doi:10.1007/s10822-016-9946-8.
-
(2016)
J. Comput. Aided Mol. Des.
, vol.30
, pp. 651-668
-
-
Gathiaka, S.1
Liu, S.2
Chiu, M.3
Yang, H.4
Stuckey, J.A.5
Kang, Y.N.6
-
82
-
-
0031080551
-
A new class of models for computing receptor-ligand binding affinities
-
Gilson, M. K., Given, J. A., and Head, M. S. (1997). A new class of models for computing receptor-ligand binding affinities. Chem. Biol. 4, 87-92.
-
(1997)
Chem. Biol.
, vol.4
, pp. 87-92
-
-
Gilson, M.K.1
Given, J.A.2
Head, M.S.3
-
83
-
-
34347224684
-
Calculation of protein-ligand binding affinities
-
Gilson, M. K., and Zhou, H.-X. (2007). Calculation of protein-ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct. 36, 21-42. doi:10.1146/annurev.biophys.36.040306.132550.
-
(2007)
Annu. Rev. Biophys. Biomol. Struct
, vol.36
, pp. 21-42
-
-
Gilson, M.K.1
Zhou, H.-X.2
-
84
-
-
0037008160
-
Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors
-
Gohlke, H., and Klebe, G. (2002). Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem. Int. Ed. 41, 2644-2676.
-
(2002)
Angew. Chem. Int. Ed.
, vol.41
, pp. 2644-2676
-
-
Gohlke, H.1
Klebe, G.2
-
86
-
-
84867365195
-
Can the Energy gap in the protein-ligand binding energy landscape be used as a descriptor in virtual ligand screening?
-
Grigoryan, A. V., Wang, H., and Cardozo, T. J. (2012). Can the Energy gap in the protein-ligand binding energy landscape be used as a descriptor in virtual ligand screening? PLoS One 7:e46532. doi:10.1371/journal.pone.0046532.
-
(2012)
PLoS One
, vol.7
, pp. e46532
-
-
Grigoryan, A.V.1
Wang, H.2
Cardozo, T.J.3
-
87
-
-
84904819424
-
Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design
-
Grinter, S. Z., and Zou, X. (2014). Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Mol. Basel Switz. 19, 10150-10176. doi:10.3390/molecules190710150.
-
(2014)
Mol. Basel Switz
, vol.19
, pp. 10150-10176
-
-
Grinter, S.Z.1
Zou, X.2
-
88
-
-
85055168154
-
Development of empirical scoring functions for predicting protein-ligand binding affinity
-
Guedes, I. A., Barreto, A. M. S., Miteva, M. A., and Dardenne, L. E. (2016). Development of empirical scoring functions for predicting protein-ligand binding affinity. Soc. Bras. Bioquim. Biol. Mol. 1-174.
-
(2016)
Soc. Bras. Bioquim. Biol. Mol.
, pp. 1-174
-
-
Guedes, I.A.1
Barreto, A.M.S.2
Miteva, M.A.3
Dardenne, L.E.4
-
89
-
-
84897767131
-
Receptor-ligand molecular docking
-
Guedes, I. A., de Magalhães, C. S., and Dardenne, L. E. (2014). Receptor-ligand molecular docking. Biophys. Rev. 6, 75-87. doi:10.1007/s12551-013-0130-2.
-
(2014)
Biophys. Rev.
, vol.6
, pp. 75-87
-
-
Guedes, I.A.1
De Magalhães, C.S.2
Dardenne, L.E.3
-
90
-
-
0011134241
-
Merck molecular force field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular interactions
-
Halgren, T. A. (1996). Merck molecular force field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular interactions. J. Comput. Chem. 17, 520-552.
-
(1996)
J. Comput. Chem.
, vol.17
, pp. 520-552
-
-
Halgren, T.A.1
-
91
-
-
1642380461
-
The problem of overfitting
-
Hawkins, D. M. (2004). The problem of overfitting. J. Chem. Inform. Comput. Sci. 44, 1-12. doi:10.1021/ci0342472.
-
(2004)
J. Chem. Inform. Comput. Sci.
, vol.44
, pp. 1-12
-
-
Hawkins, D.M.1
-
92
-
-
33751385054
-
Macroscopic models of aqueous solutions: Biological and chemical applications
-
Honig, B., Sharp, K., and Yang, A. S. (1993). Macroscopic models of aqueous solutions: Biological and chemical applications. J. Phys. Chem. 97, 1101-1109. doi:10.1021/j100108a002.
-
(1993)
J. Phys. Chem.
, vol.97
, pp. 1101-1109
-
-
Honig, B.1
Sharp, K.2
Yang, A.S.3
-
93
-
-
33750991346
-
Benchmarking sets for molecular docking
-
Huang, N., Shoichet, B. K., and Irwin, J. J. (2006). Benchmarking sets for molecular docking. J. Med. Chem. 49, 6789-6801. doi:10.1021/jm0608356.
-
(2006)
J. Med. Chem.
, vol.49
, pp. 6789-6801
-
-
Huang, N.1
Shoichet, B.K.2
Irwin, J.J.3
-
94
-
-
77957898063
-
Scoring functions and their evaluation methods for protein-ligand docking: Recent advances and future directions
-
Huang, S.-Y., Grinter, S. Z., and Zou, X. (2010). Scoring functions and their evaluation methods for protein-ligand docking: Recent advances and future directions. Phys. Chem. Chem. Phys. 12, 12899-12908. doi:10.1039/c0cp00151a.
-
(2010)
Phys. Chem. Chem. Phys.
, vol.12
, pp. 12899-12908
-
-
Huang, S.-Y.1
Grinter, S.Z.2
Zou, X.3
-
95
-
-
84955498031
-
Inexpensive method for selecting receptor structures for virtual screening
-
Huang, Z., and Wong, C. F. (2016). Inexpensive method for selecting receptor structures for virtual screening. J. Chem. Inform. Model. 56, 21-34. doi:10.1021/acs.jcim.5b00299.
-
(2016)
J. Chem. Inform. Model.
, vol.56
, pp. 21-34
-
-
Huang, Z.1
Wong, C.F.2
-
96
-
-
70349211752
-
Automated docking screens: A feasibility study
-
Irwin, J. J., Shoichet, B. K., Mysinger, M. M., Huang, N., Colizzi, F., Wassam, P., et al. (2009). Automated docking screens: A feasibility study. J. Med. Chem. 52, 5712-5720. doi:10.1021/jm9006966.
-
(2009)
J. Med. Chem.
, vol.52
, pp. 5712-5720
-
-
Irwin, J.J.1
Shoichet, B.K.2
Mysinger, M.M.3
Huang, N.4
Colizzi, F.5
Wassam, P.6
-
97
-
-
0026345750
-
Folding of chymotrypsin inhibitor 2. 1, Evidence for a two-state transition
-
Mosc
-
Jackson, S. E., and Fersht, A. R. (1991). Folding of chymotrypsin inhibitor 2. 1, Evidence for a two-state transition. Biochemistry (Mosc.) 30, 10428-10435. doi:10.1021/bi00107a010.
-
(1991)
Biochemistry
, vol.30
, pp. 10428-10435
-
-
Jackson, S.E.1
Fersht, A.R.2
-
98
-
-
0030255303
-
Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities
-
Jain, A. N. (1996). Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities. J. Comput. Aided Mol. Des. 10, 427-440.
-
(1996)
J. Comput. Aided Mol. Des.
, vol.10
, pp. 427-440
-
-
Jain, A.N.1
-
99
-
-
33749513370
-
Scoring functions for protein-ligand docking.
-
Jain, A. N. (2006). Scoring functions for protein-ligand docking. Curr. Protein Pept. Sci. 7, 407-420.
-
(2006)
Curr. Protein Pept. Sci
, vol.7
, pp. 407-420
-
-
Jain, A.N.1
-
100
-
-
41349106542
-
Recommendations for evaluation of computational methods
-
Jain, A. N., and Nicholls, A. (2008). Recommendations for evaluation of computational methods. J. Comput. Aided Mol. Des. 22, 133-139. doi:10.1007/s10822-008-9196-5.
-
(2008)
J. Comput. Aided Mol. Des.
, vol.22
, pp. 133-139
-
-
Jain, A.N.1
Nicholls, A.2
-
101
-
-
27944463552
-
An all atom energy based computational protocol for predicting binding affinities of protein-ligand complexes
-
Jain, T., and Jayaram, B. (2005). An all atom energy based computational protocol for predicting binding affinities of protein-ligand complexes. FEBS Lett. 579, 6659-6666. doi:10.1016/j.febslet.2005.10.031.
-
(2005)
FEBS Lett.
, vol.579
, pp. 6659-6666
-
-
Jain, T.1
Jayaram, B.2
-
102
-
-
85042374412
-
KDEEP: Protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks
-
Jiménez Luna, J., Skalic, M., Martinez-Rosell, G., and De Fabritiis, G. (2018). KDEEP: Protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. J. Chem. Inform. Model. 58, 287-296. doi:10.1021/acs.jcim.7b00650.
-
(2018)
J. Chem. Inform. Model.
, vol.58
, pp. 287-296
-
-
Jiménez Luna, J.1
Skalic, M.2
Martinez-Rosell, G.3
De Fabritiis, G.4
-
103
-
-
0031552362
-
Development and validation of a genetic algorithm for flexible docking
-
Jones, G., Willett, P., Glen, R. C., Leach, A. R., and Taylor, R. (1997). Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267, 727-748. doi:10.1006/jmbi.1996.0897.
-
(1997)
J. Mol. Biol.
, vol.267
, pp. 727-748
-
-
Jones, G.1
Willett, P.2
Glen, R.C.3
Leach, A.R.4
Taylor, R.5
-
104
-
-
0001195574
-
On the determination of molecular fields, I. From the variation of the viscosity of a gas with temperature
-
Jones, J. E. (1924a). On the determination of molecular fields, I. From the variation of the viscosity of a gas with temperature. Proc. R. Soc. Lond. Math. Phys. Eng. Sci. 106, 441-462. doi:10.1098/rspa.1924.0081.
-
(1924)
Proc. R. Soc. Lond. Math. Phys. Eng. Sci.
, vol.106
, pp. 441-462
-
-
Jones, J.E.1
-
105
-
-
0001195575
-
On the determination of molecular fields, II. From the equation of state of a gas
-
Jones, J. E. (1924b). On the determination of molecular fields, II. From the equation of state of a gas. Proc. R. Soc. Lond. Math. Phys. Eng. Sci. 106, 463-477. doi:10.1098/rspa.1924.0082.
-
(1924)
Proc. R. Soc. Lond. Math. Phys. Eng. Sci.
, vol.106
, pp. 463-477
-
-
Jones, J.E.1
-
106
-
-
73349107775
-
The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening
-
Kalliokoski, T., Salo, H. S., Lahtela-Kakkonen, M., and Poso, A. (2009). The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening. J. Chem. Inform. Model. 49, 2742-2748. doi:10.1021/ci900364w.
-
(2009)
J. Chem. Inform. Model.
, vol.49
, pp. 2742-2748
-
-
Kalliokoski, T.1
Salo, H.S.2
Lahtela-Kakkonen, M.3
Poso, A.4
-
107
-
-
84878052306
-
Importance of polar solvation and configurational entropy for design of antiretroviral drugs targeting HIV-1 protease
-
Kar, P., Lipowsky, R., and Knecht, V. (2013). Importance of polar solvation and configurational entropy for design of antiretroviral drugs targeting HIV-1 protease. J. Phys. Chem. B 117, 5793-5805. doi:10.1021/jp3085292.
-
(2013)
J. Phys. Chem. B
, vol.117
, pp. 5793-5805
-
-
Kar, P.1
Lipowsky, R.2
Knecht, V.3
-
108
-
-
84928563915
-
Prospective performance evaluation of selected common virtual screening tools, case study: Cyclooxygenase (COX) 1 and 2
-
Kaserer, T., Temml, V., Kutil, Z., Vanek, T., Landa, P., and Schuster, D. (2015). Prospective performance evaluation of selected common virtual screening tools, case study: Cyclooxygenase (COX) 1 and 2. Eur. J. Med. Chem. 96, 445-457. doi:10.1016/j.ejmech.2015.04.017.
-
(2015)
Eur. J. Med. Chem.
, vol.96
, pp. 445-457
-
-
Kaserer, T.1
Temml, V.2
Kutil, Z.3
Vanek, T.4
Landa, P.5
Schuster, D.6
-
109
-
-
84961177291
-
Structure-based consensus scoring scheme for selecting class A aminergic GPCR fragments
-
Kelemen, Á. A., Kiss, R., Ferenczy, G. G., Kovács, L., Flachner, B., Lõrincz, Z., et al. (2016). Structure-based consensus scoring scheme for selecting class A aminergic GPCR fragments. J. Chem. Inform. Model. 56, 412-422. doi:10.1021/acs.jcim.5b00598.
-
(2016)
J. Chem. Inform. Model.
, vol.56
, pp. 412-422
-
-
Kelemen, Á.A.1
Kiss, R.2
Ferenczy, G.G.3
Kovács, L.4
Flachner, B.5
Lõrincz, Z.6
-
111
-
-
84941117199
-
Comparative assessment of machine-learning scoring functions on PDBbind 2013
-
Khamis, M. A., and Gomaa, W. (2015). Comparative assessment of machine-learning scoring functions on PDBbind 2013. Eng. Appl. Artif. Intell. 45, 136-151. doi:10.1016/j.engappai.2015.06.021.
-
(2015)
Eng. Appl. Artif. Intell.
, vol.45
, pp. 136-151
-
-
Khamis, M.A.1
Gomaa, W.2
-
112
-
-
84975045758
-
AutoDock VinaXB: Implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina
-
Koebel, M. R., Schmadeke, G., Posner, R. G., and Sirimulla, S. (2016). AutoDock VinaXB: Implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina. J. Cheminform. 8:27. doi:10.1186/s13321-016-0139-1.
-
(2016)
J. Cheminform.
, vol.8
, pp. 27
-
-
Koebel, M.R.1
Schmadeke, G.2
Posner, R.G.3
Sirimulla, S.4
-
113
-
-
73449145345
-
Docking screens: Right for the right reasons?
-
Kolb, P., and Irwin, J. J. (2009). Docking screens: Right for the right reasons? Curr. Top. Med. Chem. 9, 755-770.
-
(2009)
Curr. Top. Med. Chem.
, vol.9
, pp. 755-770
-
-
Kolb, P.1
Irwin, J.J.2
-
114
-
-
85042150443
-
Importance of protein flexibility in molecular recognition: A case study on Type-I1/2 inhibitors of ALK
-
Kong, X., Sun, H., Pan, P., Zhu, F., Chang, S., Xu, L., et al. (2018). Importance of protein flexibility in molecular recognition: A case study on Type-I1/2 inhibitors of ALK. Phys. Chem. Chem. Phys. 20, 4851-4863. doi:10.1039/C7CP08241J.
-
(2018)
Phys. Chem. Chem. Phys.
, vol.20
, pp. 4851-4863
-
-
Kong, X.1
Sun, H.2
Pan, P.3
Zhu, F.4
Chang, S.5
Xu, L.6
-
115
-
-
84861499934
-
Potential and limitations of ensemble docking
-
Korb, O., Olsson, T. S. G., Bowden, S. J., Hall, R. J., Verdonk, M. L., Liebeschuetz, J. W., et al. (2012). Potential and limitations of ensemble docking. J. Chem. Inform. Model. 52, 1262-1274. doi:10.1021/ci2005934.
-
(2012)
J. Chem. Inform. Model.
, vol.52
, pp. 1262-1274
-
-
Korb, O.1
Olsson, T.S.G.2
Bowden, S.J.3
Hall, R.J.4
Verdonk, M.L.5
Liebeschuetz, J.W.6
-
116
-
-
78649517318
-
Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets
-
Kramer, C., and Gedeck, P. (2010). Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets. J. Chem. Inform. Model. 50, 1961-1969. doi:10.1021/ci100264e.
-
(2010)
J. Chem. Inform. Model.
, vol.50
, pp. 1961-1969
-
-
Kramer, C.1
Gedeck, P.2
-
117
-
-
84862276184
-
The experimental uncertainty of heterogeneous public ki data
-
Kramer, C., Kalliokoski, T., Gedeck, P., and Vulpetti, A. (2012). The experimental uncertainty of heterogeneous public ki data. J. Med. Chem. 55, 5165-5173. doi:10.1021/jm300131x.
-
(2012)
J. Med. Chem.
, vol.55
, pp. 5165-5173
-
-
Kramer, C.1
Kalliokoski, T.2
Gedeck, P.3
Vulpetti, A.4
-
118
-
-
15244346501
-
LigScore: A novel scoring function for predicting binding affinities
-
Krammer, A., Kirchhoff, P. D., Jiang, X., Venkatachalam, C. M., and Waldman, M. (2005). LigScore: A novel scoring function for predicting binding affinities. J. Mol. Graph. Model. 23, 395-407doi:10.1016/j.jmgm.2004.11.007.
-
(2005)
J. Mol. Graph. Model.
, vol.23
, pp. 395-407
-
-
Krammer, A.1
Kirchhoff, P.D.2
Jiang, X.3
Venkatachalam, C.M.4
Waldman, M.5
-
119
-
-
84912572423
-
Which three-dimensional characteristics make efficient inhibitors of protein-protein interactions?
-
Kuenemann, M. A., Bourbon, L. M. L., Labbé, C. M., Villoutreix, B. O., and Sperandio, O. (2014). Which three-dimensional characteristics make efficient inhibitors of protein-protein interactions? J. Chem. Inform. Model. 54, 3067-3079. doi:10.1021/ci500487q.
-
(2014)
J. Chem. Inform. Model.
, vol.54
, pp. 3067-3079
-
-
Kuenemann, M.A.1
Bourbon, L.M.L.2
Labb, C.M.3
Villoutreix, B.O.4
Sperandio, O.5
-
120
-
-
84555169381
-
Rationalizing tight ligand binding through cooperative interaction networks
-
Kuhn, B., Fuchs, J. E., Reutlinger, M., Stahl, M., and Taylor, N. R. (2011). Rationalizing tight ligand binding through cooperative interaction networks. J. Chem. Inform. Model. 51, 3180-3198. doi:10.1021/ci200319e.
-
(2011)
J. Chem. Inform. Model.
, vol.51
, pp. 3180-3198
-
-
Kuhn, B.1
Fuchs, J.E.2
Reutlinger, M.3
Stahl, M.4
Taylor, N.R.5
-
121
-
-
84921920564
-
Theory and applications of covalent docking in drug discovery: Merits and pitfalls
-
Kumalo, H. M., Bhakat, S., and Soliman, M. E. S. (2015). Theory and applications of covalent docking in drug discovery: Merits and pitfalls. Mol. Basel Switz. 20, 1984-2000. doi:10.3390/molecules20021984.
-
(2015)
Mol. Basel Switz.
, vol.20
, pp. 1984-2000
-
-
Kumalo, H.M.1
Bhakat, S.2
Soliman, M.E.S.3
-
122
-
-
85027857854
-
Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R grand challenge 2
-
Kurkcuoglu, Z., Koukos, P. I., Citro, N., Trellet, M. E., Rodrigues, J. P. G. L. M., Moreira, I. S., et al. (2018). Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R grand challenge 2. J. Comput. Aided Mol. Des. 32, 175-185. doi:10.1007/s10822-017-0049-y.
-
(2018)
J. Comput. Aided Mol. des
, vol.32
, pp. 175-185
-
-
Kurkcuoglu, Z.1
Koukos, P.I.2
Citro, N.3
Trellet, M.E.4
Rodrigues, J.P.G.L.M.5
Moreira, I.S.6
-
123
-
-
84979850761
-
MTiOpenScreen: A web server for structure-based virtual screening
-
Labbé, C. M., Rey, J., Lagorce, D., Vavruša, M., Becot, J., Sperandio, O., et al. (2015). MTiOpenScreen: A web server for structure-based virtual screening. Nucleic Acids Res. 43, W448-W454. doi:10.1093/nar/gkv306.
-
(2015)
Nucleic Acids Res.
, vol.43
, pp. W448-W454
-
-
Labb, C.M.1
Rey, J.2
Lagorce, D.3
Vavruša, M.4
Becot, J.5
Sperandio, O.6
-
124
-
-
84938151793
-
Benchmarking data sets for the evaluation of virtual ligand screening methods: Review and perspectives
-
Lagarde, N., Zagury, J.-F., and Montes, M. (2015). Benchmarking data sets for the evaluation of virtual ligand screening methods: Review and perspectives. J. Chem. Inform. Model. 55, 1297-1307. doi:10.1021/acs.jcim.5b00090.
-
(2015)
J. Chem. Inform. Model.
, vol.55
, pp. 1297-1307
-
-
Lagarde, N.1
Zagury, J.-F.2
Montes, M.3
-
125
-
-
85029005758
-
Ligand-biased ensemble receptor docking (LigBEnD): A hybrid ligand/receptor structure-based approach
-
Lam, P. C.-H., Abagyan, R., and Totrov, M. (2017). Ligand-biased ensemble receptor docking (LigBEnD): A hybrid ligand/receptor structure-based approach. J. Comput. Aided Mol. Des. 32, 187-198. doi:10.1007/s10822-017-0058-x.
-
(2017)
J. Comput. Aided Mol. Des.
, vol.32
, pp. 187-198
-
-
Lam, P.C.-H.1
Abagyan, R.2
Totrov, M.3
-
126
-
-
33745032291
-
Water mediation in protein folding and molecular recognition
-
Levy, Y., and Onuchic, J. N. (2006). Water mediation in protein folding and molecular recognition. Annu. Rev. Biophys. Biomol. Struct. 35, 389-415. doi:10.1146/annurev.biophys.35.040405.102134.
-
(2006)
Annu. Rev. Biophys. Biomol. Struct
, vol.35
, pp. 389-415
-
-
Levy, Y.1
Onuchic, J.N.2
-
127
-
-
85034103749
-
Consensus scoring model for the molecular docking study of mTOR kinase inhibitor
-
Li, D.-D., Meng, X.-F., Wang, Q., Yu, P., Zhao, L.-G., Zhang, Z.-P., et al. (2018). Consensus scoring model for the molecular docking study of mTOR kinase inhibitor. J. Mol. Graph. Model. 79, 81-87. doi:10.1016/j.jmgm.2017.11.003.
-
(2018)
J. Mol. Graph. Model.
, vol.79
, pp. 81-87
-
-
Li, D.-D.1
Meng, X.-F.2
Wang, Q.3
Yu, P.4
Zhao, L.-G.5
Zhang, Z.-P.6
-
128
-
-
85044292801
-
The impact of protein structure and sequence similarity on the accuracy of machine-learning scoring functions for binding affinity prediction
-
Li, H., Peng, J., Leung, Y., Leung, K.-S., Wong, M.-H., Lu, G., et al. (2018). The impact of protein structure and sequence similarity on the accuracy of machine-learning scoring functions for binding affinity prediction. Biomolecules 8:12. doi:10.3390/biom8010012.
-
(2018)
Biomolecules
, vol.8
, pp. 12
-
-
Li, H.1
Peng, J.2
Leung, Y.3
Leung, K.-S.4
Wong, M.-H.5
Lu, G.6
-
129
-
-
85044295465
-
Assessing protein-ligand interaction scoring functions with the CASF-2013 benchmark
-
Li, Y., Su, M., Liu, Z., Li, J., Liu, J., Han, L., et al. (2018). Assessing protein-ligand interaction scoring functions with the CASF-2013 benchmark. Nat. Protoc. 13, 666-680. doi:10.1038/nprot.2017.114.
-
(2018)
Nat. Protoc.
, vol.13
, pp. 666-680
-
-
Li, Y.1
Su, M.2
Liu, Z.3
Li, J.4
Liu, J.5
Han, L.6
-
130
-
-
84875428269
-
ID-score: A new empirical scoring function based on a comprehensive set of descriptors related to protein-ligand interactions
-
Li, G.-B., Yang, L.-L., Wang, W.-J., Li, L.-L., and Yang, S.-Y. (2013). ID-score: A new empirical scoring function based on a comprehensive set of descriptors related to protein-ligand interactions. J. Chem. Inform. Model. 53, 592-600. doi:10.1021/ci300493w.
-
(2013)
J. Chem. Inform. Model.
, vol.53
, pp. 592-600
-
-
Li, G.-B.1
Yang, L.-L.2
Wang, W.-J.3
Li, L.-L.4
Yang, S.-Y.5
-
131
-
-
84938280812
-
Low-quality structural and interaction data improves binding affinity prediction via random forest
-
Li, H., Leung, K.-S., Wong, M.-H., and Ballester, P. (2015a). Low-quality structural and interaction data improves binding affinity prediction via random forest. Molecules 20, 10947-10962. doi:10.3390/molecules200610947.
-
(2015)
Molecules
, vol.20
, pp. 10947-10962
-
-
Li, H.1
Leung, K.-S.2
Wong, M.-H.3
Ballester, P.4
-
132
-
-
84923588607
-
Improving autodock vina using random forest: The growing accuracy of binding affinity prediction by the effective exploitation of larger data sets
-
Li, H., Leung, K.-S., Wong, M.-H., and Ballester, P. J. (2015b). Improving autodock vina using random forest: The growing accuracy of binding affinity prediction by the effective exploitation of larger data sets. Mol. Inform. 34, 115-126. doi:10.1002/minf.201400132.
-
(2015)
Mol. Inform.
, vol.34
, pp. 115-126
-
-
Li, H.1
Leung, K.-S.2
Wong, M.-H.3
Ballester, P.J.4
-
133
-
-
84906829436
-
Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study
-
Li, H., Leung, K.-S., Wong, M.-H., and Ballester, P. J. (2014a). Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. BMC Bioinformatics 15:291. doi:10.1186/1471-2105-15-291.
-
(2014)
BMC Bioinformatics
, vol.15
, pp. 291
-
-
Li, H.1
Leung, K.-S.2
Wong, M.-H.3
Ballester, P.J.4
-
134
-
-
84945454057
-
The impact of docking pose generation error on the prediction of binding affinity
-
eds C. D. Serio, P. Liò, A. Nonis, and R. Tagliaferri Berlin: Springer International Publishing
-
Li, H., Leung, K.-S., Wong, M.-H., and Ballester, P. J. (2014b). "The impact of docking pose generation error on the prediction of binding affinity, " in Computational Intelligence Methods for Bioinformatics and Biostatistics Lecture Notes in Computer Science, eds C. D. Serio, P. Liò, A. Nonis, and R. Tagliaferri (Berlin: Springer International Publishing), 231-241. doi:10.1007/978-3-319-24462-4-20.
-
(2014)
Computational Intelligence Methods for Bioinformatics and Biostatistics Lecture Notes in Computer Science
, pp. 231-241
-
-
Li, H.1
Leung, K.-S.2
Wong, M.-H.3
Ballester, P.J.4
-
135
-
-
84903287174
-
Comparative assessment of scoring functions on an updated benchmark: 2, evaluation methods and general results
-
Li, Y., Han, L., Liu, Z., and Wang, R. (2014c). Comparative assessment of scoring functions on an updated benchmark: 2, evaluation methods and general results. J. Chem. Inform. Model. 54, 1717-1736. doi:10.1021/ci500081m.
-
(2014)
J. Chem. Inform. Model.
, vol.54
, pp. 1717-1736
-
-
Li, Y.1
Han, L.2
Liu, Z.3
Wang, R.4
-
136
-
-
84925387258
-
Classification of current scoring functions
-
Liu, J., and Wang, R. (2015). Classification of current scoring functions. J. Chem. Inform. Model. 55, 475-482. doi:10.1021/ci500731a.
-
(2015)
J. Chem. Inform. Model.
, vol.55
, pp. 475-482
-
-
Liu, J.1
Wang, R.2
-
137
-
-
84862224304
-
Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1)
-
doi: 10.1371/journal.pone.0038086
-
Liu, S., Fu, R., Zhou, L.-H., and Chen, S.-P. (2012). Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1). PLoS One 7:e38086. doi: 10.1371/journal.pone.0038086.
-
(2012)
PLoS One
, vol.7
, pp. e38086
-
-
Liu, S.1
Fu, R.2
Zhou, L.-H.3
Chen, S.-P.4
-
138
-
-
84929141895
-
PDB-wide collection of binding data: Current status of the PDBbind database
-
Liu, Z., Li, Y., Han, L., Li, J., Liu, J., Zhao, Z., et al. (2015). PDB-wide collection of binding data: Current status of the PDBbind database. Bioinformatics 31, 405-412. doi:10.1093/bioinformatics/btu626.
-
(2015)
Bioinformatics
, vol.31
, pp. 405-412
-
-
Liu, Z.1
Li, Y.2
Han, L.3
Li, J.4
Liu, J.5
Zhao, Z.6
-
139
-
-
85013653033
-
Forging the basis for developing protein-ligand interaction scoring functions
-
Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., et al. (2017). Forging the basis for developing protein-ligand interaction scoring functions. Acc. Chem. Res. 50, 302-309. doi:10.1021/acs.accounts.6b00491.
-
(2017)
Acc. Chem. Res.
, vol.50
, pp. 302-309
-
-
Liu, Z.1
Su, M.2
Han, L.3
Liu, J.4
Yang, Q.5
Li, Y.6
-
140
-
-
0035848409
-
Customized versus universal scoring functions: Application to class I MHC-peptide binding free energy predictions
-
Logean, A., Sette, A., and Rognan, D. (2001). Customized versus universal scoring functions: Application to class I MHC-peptide binding free energy predictions. Bioorg. Med. Chem. Lett. 11, 675-679.
-
(2001)
Bioorg. Med. Chem. Lett.
, vol.11
, pp. 675-679
-
-
Logean, A.1
Sette, A.2
Rognan, D.3
-
141
-
-
84913544750
-
Covalent docking of large libraries for the discovery of chemical probes
-
London, N., Miller, R. M., Krishnan, S., Uchida, K., Irwin, J. J., Eidam, O., et al. (2014). Covalent docking of large libraries for the discovery of chemical probes. Nat. Chem. Biol. 10, 1066-1072. doi:10.1038/nchembio.1666.
-
(2014)
Nat. Chem. Biol.
, vol.10
, pp. 1066-1072
-
-
London, N.1
Miller, R.M.2
Krishnan, S.3
Uchida, K.4
Irwin, J.J.5
Eidam, O.6
-
142
-
-
76249106208
-
Let's not forget tautomers
-
Martin, Y. C. (2009). Let's not forget tautomers. J. Comput. Aided Mol. Des. 23, 693-704. doi:10.1007/s10822-009-9303-2.
-
(2009)
J. Comput. Aided Mol. Des.
, vol.23
, pp. 693-704
-
-
Martin, Y.C.1
-
143
-
-
84962526213
-
Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)
-
Martin, Y. C., Abagyan, R., Ferenczy, G. G., Gillet, V. J., Oprea, T. I., Ulander, J., et al. (2016). Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015). Pure Appl. Chem. 88, 239-264. doi:10.1515/pac-2012-1204.
-
(2016)
Pure Appl. Chem.
, vol.88
, pp. 239-264
-
-
Martin, Y.C.1
Abagyan, R.2
Ferenczy, G.G.3
Gillet, V.J.4
Oprea, T.I.5
Ulander, J.6
-
144
-
-
78650725078
-
Applications and success stories in virtual screening
-
ed. C. Sotriffer Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA
-
Matter, H., and Sotriffer, C. (2011). "Applications and success stories in virtual screening, " in Methods and Principles in Medicinal Chemistry, ed. C. Sotriffer (Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA), 319-358.
-
(2011)
Methods and Principles in Medicinal Chemistry
, pp. 319-358
-
-
Matter, H.1
Sotriffer, C.2
-
145
-
-
85046247663
-
NAMD goes quantum: An integrative suite for hybrid simulations
-
Melo, M. C. R., Bernardi, R. C., Rudack, T., Scheurer, M., Riplinger, C., Phillips, J. C., et al. (2018). NAMD goes quantum: An integrative suite for hybrid simulations. Nat. Methods 15, 351-354. doi:10.1038/nmeth.4638.
-
(2018)
Nat. Methods
, vol.15
, pp. 351-354
-
-
Melo, M.C.R.1
Bernardi, R.C.2
Rudack, T.3
Scheurer, M.4
Riplinger, C.5
Phillips, J.C.6
-
146
-
-
84986432941
-
Automated docking with grid-based energy evaluation
-
Meng, E. C., Shoichet, B. K., and Kuntz, I. D. (1992). Automated docking with grid-based energy evaluation. J. Comput. Chem. 13, 505-524. doi:10.1002/jcc.540130412.
-
(1992)
J. Comput. Chem
, vol.13
, pp. 505-524
-
-
Meng, E.C.1
Shoichet, B.K.2
Kuntz, I.D.3
-
147
-
-
84950162061
-
Why and how have drug discovery strategies in pharma changed? What are the new mindsets?
-
Mignani, S., Huber, S., Tomás, H., Rodrigues, J., and Majoral, J.-P. (2016). Why and how have drug discovery strategies in pharma changed? What are the new mindsets? Drug Discov. Today 21, 239-249. doi:10.1016/j.drudis.2015.09.007.
-
(2016)
Drug Discov. Today
, vol.21
, pp. 239-249
-
-
Mignani, S.1
Huber, S.2
Tomás, H.3
Rodrigues, J.4
Majoral, J.-P.5
-
148
-
-
0030867610
-
Ligand binding to proteins: The binding landscape model
-
Miller, D. W., and Dill, K. A. (1997). Ligand binding to proteins: The binding landscape model. Protein Sci. 6, 2166-2179. doi:10.1002/pro.5560061011.
-
(1997)
Protein Sci.
, vol.6
, pp. 2166-2179
-
-
Miller, D.W.1
Dill, K.A.2
-
149
-
-
40349087133
-
Towards the development of universal, fast and highly accurate docking/scoring methods: A long way to go: Docking/scoring methods-a review
-
Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., and Corbeil, C. R. (2009). Towards the development of universal, fast and highly accurate docking/scoring methods: A long way to go: Docking/scoring methods-a review. Br. J. Pharmacol. 153, S7-S26. doi:10.1038/sj.bjp.0707515.
-
(2009)
Br. J. Pharmacol.
, vol.153
, pp. S7-S26
-
-
Moitessier, N.1
Englebienne, P.2
Lee, D.3
Lawandi, J.4
Corbeil, C.R.5
-
150
-
-
79960990847
-
Chemical and structural lessons from recent successes in protein-protein interaction inhibition (2P2I)
-
Morelli, X., Bourgeas, R., and Roche, P. (2011). Chemical and structural lessons from recent successes in protein-protein interaction inhibition (2P2I). Curr. Opin. Chem. Biol. 15, 475-481. doi:10.1016/j.cbpa.2011.05.024.
-
(2011)
Curr. Opin. Chem. Biol.
, vol.15
, pp. 475-481
-
-
Morelli, X.1
Bourgeas, R.2
Roche, P.3
-
151
-
-
33749242403
-
PMF scoring revisited
-
Muegge, I. (2006). PMF scoring revisited. J. Med. Chem. 49, 5895-5902. doi:10.1021/jm050038s.
-
(2006)
J. Med. Chem.
, vol.49
, pp. 5895-5902
-
-
Muegge, I.1
-
152
-
-
84933041169
-
New drugs cost US$2.6 billion to develop
-
Mullard, A. (2014). New drugs cost US$2.6 billion to develop. Nat. Rev. Drug Discov. 13, 877-877. doi:10.1038/nrd4507.
-
(2014)
Nat. Rev. Drug Discov.
, vol.13
, pp. 877-877
-
-
Mullard, A.1
-
153
-
-
84969512867
-
WScore: A flexible and accurate treatment of explicit water molecules in ligand-receptor docking
-
Murphy, R. B., Repasky, M. P., Greenwood, J. R., Tubert-Brohman, I., Jerome, S., Annabhimoju, R., et al. (2016). WScore: A flexible and accurate treatment of explicit water molecules in ligand-receptor docking. J. Med. Chem. 59, 4364-4384. doi:10.1021/acs.jmedchem.6b00131.
-
(2016)
J. Med. Chem.
, vol.59
, pp. 4364-4384
-
-
Murphy, R.B.1
Repasky, M.P.2
Greenwood, J.R.3
Tubert-Brohman, I.4
Jerome, S.5
Annabhimoju, R.6
-
154
-
-
84864264343
-
Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking
-
Mysinger, M. M., Carchia, M., Irwin, J. J., and Shoichet, B. K. (2012). Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J. Med. Chem. 55, 6582-6594. doi:10.1021/jm300687e.
-
(2012)
J. Med. Chem.
, vol.55
, pp. 6582-6594
-
-
Mysinger, M.M.1
Carchia, M.2
Irwin, J.J.3
Shoichet, B.K.4
-
155
-
-
85061217218
-
Mathematical deep learning for pose and binding affinity prediction and ranking in D3R grand challenges
-
Epub ahead of print
-
Nguyen, D. D., Cang, Z., Wu, K., Wang, M., Cao, Y., and Wei, G.-W. (2018). Mathematical deep learning for pose and binding affinity prediction and ranking in D3R grand challenges. J. Comput. Aided Mol. Des. [Epub ahead of print].
-
(2018)
J. Comput. Aided Mol. Des.
-
-
Nguyen, D.D.1
Cang, Z.2
Wu, K.3
Wang, M.4
Cao, Y.5
Wei, G.-W.6
-
156
-
-
9144230700
-
In situ extension as an approach for identifying novel α-amylase inhibitors
-
Numao, S., Damager, I., Li, C., Wrodnigg, T. M., Begum, A., Overall, C. M., et al. (2004). In situ extension as an approach for identifying novel α-amylase inhibitors. J. Biol. Chem. 279, 48282-48291. doi:10.1074/jbc.M406804200.
-
(2004)
J. Biol. Chem.
, vol.279
, pp. 48282-48291
-
-
Numao, S.1
Damager, I.2
Li, C.3
Wrodnigg, T.M.4
Begum, A.5
Overall, C.M.6
-
157
-
-
84906539667
-
Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging
-
Nunes-Alves, A., and Arantes, G. M. (2014). Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging. J. Chem. Inform. Model. 54, 2309-2319. doi:10.1021/ci500301s.
-
(2014)
J. Chem. Inform. Model.
, vol.54
, pp. 2309-2319
-
-
Nunes-Alves, A.1
Arantes, G.M.2
-
158
-
-
84872598296
-
Covalent dock: Automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints
-
Ouyang, X., Zhou, S., Su, C. T. T., Ge, Z., Li, R., and Kwoh, C. K. (2013). Covalent dock: Automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints. J. Comput. Chem. 34, 326-336. doi:10.1002/jcc.23136.
-
(2013)
J. Comput. Chem.
, vol.34
, pp. 326-336
-
-
Ouyang, X.1
Zhou, S.2
Su, C.T.T.3
Ge, Z.4
Li, R.5
Kwoh, C.K.6
-
159
-
-
85017008018
-
Software for molecular docking: A review
-
Pagadala, N. S., Syed, K., and Tuszynski, J. (2017). Software for molecular docking: A review. Biophys. Rev. 9, 91-102. doi:10.1007/s12551-016-0247-1.
-
(2017)
Biophys. Rev
, vol.9
, pp. 91-102
-
-
Pagadala, N.S.1
Syed, K.2
Tuszynski, J.3
-
160
-
-
65249095339
-
Evaluating docking methods for prediction of binding affinities of small molecules to the g protein βγ subunits
-
Park, M.-S., Dessal, A. L., Smrcka, A. V., and Stern, H. A. (2009). Evaluating docking methods for prediction of binding affinities of small molecules to the g protein βγ subunits. J. Chem. Inform. Model. 49, 437-443. doi:10.1021/ci800384q.
-
(2009)
J. Chem. Inform. Model.
, vol.49
, pp. 437-443
-
-
Park, M.-S.1
Dessal, A.L.2
Smrcka, A.V.3
Stern, H.A.4
-
161
-
-
77953325281
-
Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles
-
Park, S.-J., Kufareva, I., and Abagyan, R. (2010). Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles. J. Comput. Aided Mol. Des. 24, 459-471. doi:10.1007/s10822-010-9362-4.
-
(2010)
J. Comput. Aided Mol. Des.
, vol.24
, pp. 459-471
-
-
Park, S.-J.1
Kufareva, I.2
Abagyan, R.3
-
162
-
-
84978269171
-
Empirical scoring functions for affinity prediction of protein-ligand complexes
-
Pason, L. P., and Sotriffer, C. A. (2016). Empirical scoring functions for affinity prediction of protein-ligand complexes. Mol. Inform. 35, 541-548. doi:10.1002/minf.201600048.
-
(2016)
Mol. Inform.
, vol.35
, pp. 541-548
-
-
Pason, L.P.1
Sotriffer, C.A.2
-
163
-
-
73349111930
-
Scoring ensembles of docked protein: Ligand interactions for virtual lead optimization
-
Paulsen, J. L., and Anderson, A. C. (2009). Scoring ensembles of docked protein: Ligand interactions for virtual lead optimization. J. Chem. Inform. Model. 49:2813. doi:10.1021/ci9003078.
-
(2009)
J. Chem. Inform. Model.
, vol.49
, pp. 2813
-
-
Paulsen, J.L.1
Anderson, A.C.2
-
164
-
-
85042127714
-
Ranking power of the SQM/COSMO scoring function on carbonic anhydrase II-inhibitor complexes
-
Pecina, A., Brynda, J., Vrzal, L., Gnanasekaran, R., Horejší, M., Eyrilmez, S. M., et al. (2018). Ranking power of the SQM/COSMO scoring function on carbonic anhydrase II-inhibitor complexes. Chem Phys Chem 19, 873-879. doi:10.1002/cphc.201701104.
-
(2018)
Chem Phys Chem
, vol.19
, pp. 873-879
-
-
Pecina, A.1
Brynda, J.2
Vrzal, L.3
Gnanasekaran, R.4
Horejší, M.5
Eyrilmez, S.M.6
-
165
-
-
85008475964
-
Boosting docking-based virtual screening with deep learning
-
Pereira, J. C., Caffarena, E. R., and dos Santos, C. N. (2016). Boosting docking-based virtual screening with deep learning. J. Chem. Inform. Model. 56, 2495-2506. doi:10.1021/acs.jcim.6b00355.
-
(2016)
J. Chem. Inform. Model.
, vol.56
, pp. 2495-2506
-
-
Pereira, J.C.1
Caffarena, E.R.2
Dos Santos, C.N.3
-
166
-
-
84878652881
-
The role of protonation states in ligand-receptor recognition and binding
-
Petukh, M., Stefl, S., and Alexov, E. (2013). The role of protonation states in ligand-receptor recognition and binding. Curr. Pharm. Des. 19, 4182-4190.
-
(2013)
Curr. Pharm. Des.
, vol.19
, pp. 4182-4190
-
-
Petukh, M.1
Stefl, S.2
Alexov, E.3
-
167
-
-
0036893503
-
Kinase inhibitors and the case for CH...O hydrogen bonds in protein-ligand binding
-
Pierce, A. C., Sandretto, K. L., and Bemis, G. W. (2002). Kinase inhibitors and the case for CH...O hydrogen bonds in protein-ligand binding. Proteins 49, 567-576. doi:10.1002/prot.10259.
-
(2002)
Proteins
, vol.49
, pp. 567-576
-
-
Pierce, A.C.1
Sandretto, K.L.2
Bemis, G.W.3
-
168
-
-
85008692166
-
CSM-lig: A web server for assessing and comparing protein-small molecule affinities
-
Pires, D. E. V., and Ascher, D. B. (2016). CSM-lig: A web server for assessing and comparing protein-small molecule affinities. Nucleic Acids Res. 44, W557-W561. doi:10.1093/nar/gkw390.
-
(2016)
Nucleic Acids Res.
, vol.44
, pp. W557-W561
-
-
Pires, D.E.V.1
Ascher, D.B.2
-
169
-
-
0029450365
-
Hydration in drug design. 1. Multiple hydrogen-bonding features of water molecules in mediating protein-ligand interactions
-
Poornima, C. S., and Dean, P. M. (1995). Hydration in drug design. 1. Multiple hydrogen-bonding features of water molecules in mediating protein-ligand interactions. J. Comput. Aided Mol. Des. 9, 500-512.
-
(1995)
J. Comput. Aided Mol. Des.
, vol.9
, pp. 500-512
-
-
Poornima, C.S.1
Dean, P.M.2
-
170
-
-
4043171970
-
The Gb/SA continuum model for solvation. A fast analytical method for the calculation of approximate born radii
-
Qiu, D., Shenkin, P. S., Hollinger, F. P., and Still, W. C. (1997). The GB/SA continuum model for solvation. A fast analytical method for the calculation of approximate born radii. J. Phys. Chem. A 101, 3005-3014. doi:10.1021/jp961992r.
-
(1997)
J. Phys. Chem. a
, vol.101
, pp. 3005-3014
-
-
Qiu, D.1
Shenkin, P.S.2
Hollinger, F.P.3
Still, W.C.4
-
171
-
-
85018558434
-
Protein-ligand scoring with convolutional neural networks
-
Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J., and Koes, D. R. (2017). Protein-ligand scoring with convolutional neural networks. J. Chem. Inform. Model. 57, 942-957. doi:10.1021/acs.jcim.6b00740.
-
(2017)
J. Chem. Inform. Model.
, vol.57
, pp. 942-957
-
-
Ragoza, M.1
Hochuli, J.2
Idrobo, E.3
Sunseri, J.4
Koes, D.R.5
-
172
-
-
22244451417
-
Large-scale validation of a quantum mechanics based scoring function: Predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes
-
Raha, K., and Merz, K. M. (2005). Large-scale validation of a quantum mechanics based scoring function: Predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes. J. Med. Chem. 48, 4558-4575. doi:10.1021/jm048973n.
-
(2005)
J. Med. Chem
, vol.48
, pp. 4558-4575
-
-
Raha, K.1
Merz, K.M.2
-
173
-
-
0030599010
-
A fast flexible docking method using an incremental construction algorithm
-
Rarey, M., Kramer, B., Lengauer, T., and Klebe, G. (1996). A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 261, 470-489. doi:10.1006/jmbi.1996.0477.
-
(1996)
J. Mol. Biol.
, vol.261
, pp. 470-489
-
-
Rarey, M.1
Kramer, B.2
Lengauer, T.3
Klebe, G.4
-
174
-
-
84953216380
-
AutoDockFR: Advances in protein-ligand docking with explicitly specified binding site flexibility
-
Ravindranath, P. A., Forli, S., Goodsell, D. S., Olson, A. J., and Sanner, M. F. (2015). AutoDockFR: Advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS Comput. Biol. 11:e1004586. doi:10.1371/journal.pcbi.1004586.
-
(2015)
PLoS Comput. Biol.
, vol.11
, pp. e1004586
-
-
Ravindranath, P.A.1
Forli, S.2
Goodsell, D.S.3
Olson, A.J.4
Sanner, M.F.5
-
175
-
-
85041127181
-
Decoys selection in benchmarking datasets: Overview and perspectives
-
Réau, M., Langenfeld, F., Zagury, J.-F., Lagarde, N., and Montes, M. (2018). Decoys selection in benchmarking datasets: Overview and perspectives. Front. Pharmacol. 9:11. doi:10.3389/fphar.2018.00011.
-
(2018)
Front. Pharmacol.
, vol.9
, pp. 11
-
-
Réau, M.1
Langenfeld, F.2
Zagury, J.-F.3
Lagarde, N.4
Montes, M.5
-
176
-
-
84876295685
-
Free enthalpies of replacing water molecules in protein binding pockets
-
Riniker, S., Barandun, L. J., Diederich, F., Krämer, O., Steffen, A., and van Gunsteren, W. F. (2012). Free enthalpies of replacing water molecules in protein binding pockets. J. Comput. Aided Mol. Des. 26, 1293-1309. doi:10.1007/s10822-012-9620-8.
-
(2012)
J. Comput. Aided Mol. Des.
, vol.26
, pp. 1293-1309
-
-
Riniker, S.1
Barandun, L.J.2
Diederich, F.3
Krämer, O.4
Steffen, A.5
Van Gunsteren, W.F.6
-
177
-
-
85013656808
-
The impact of in silico screening in the discovery of novel and safer drug candidates
-
Rognan, D. (2017). The impact of in silico screening in the discovery of novel and safer drug candidates. Pharmacol. Ther. 175, 47-66. doi:10.1016/j.pharmthera.2017.02.034.
-
(2017)
Pharmacol. Ther.
, vol.175
, pp. 47-66
-
-
Rognan, D.1
-
178
-
-
0033523959
-
Predicting binding affinities of protein ligands from three-dimensional models: Application to peptide binding to class I major histocompatibility proteins
-
Rognan, D., Lauemoller, S. L., Holm, A., Buus, S., and Tschinke, V. (1999). Predicting binding affinities of protein ligands from three-dimensional models: Application to peptide binding to class I major histocompatibility proteins. J. Med. Chem. 42, 4650-4658.
-
(1999)
J. Med. Chem
, vol.42
, pp. 4650-4658
-
-
Rognan, D.1
Lauemoller, S.L.2
Holm, A.3
Buus, S.4
Tschinke, V.5
-
179
-
-
84969593568
-
Ligand-binding affinity estimates supported by quantum-mechanical methods
-
Ryde, U., and Söderhjelm, P. (2016). Ligand-binding affinity estimates supported by quantum-mechanical methods. Chem. Rev. 116, 5520-5566. doi:10.1021/acs.chemrev.5b00630.
-
(2016)
Chem. Rev.
, vol.116
, pp. 5520-5566
-
-
Ryde, U.1
Söderhjelm, P.2
-
180
-
-
84983535300
-
Interaction with specific HSP90 residues as a scoring function: Validation in the D3R Grand Challenge 2015
-
Santos-Martins, D. (2016). Interaction with specific HSP90 residues as a scoring function: Validation in the D3R Grand Challenge 2015. J. Comput. Aided Mol. Des. 30, 731-742. doi:10.1007/s10822-016-9943-y.
-
(2016)
J. Comput. Aided Mol. Des.
, vol.30
, pp. 731-742
-
-
Santos-Martins, D.1
-
181
-
-
84906569752
-
AutoDock4Zn: An improved autodock force field for small-molecule docking to zinc metalloproteins
-
Santos-Martins, D., Forli, S., Ramos, M. J., and Olson, A. J. (2014). AutoDock4Zn: An improved autodock force field for small-molecule docking to zinc metalloproteins. J. Chem. Inform. Model. 54, 2371-2379. doi:10.1021/ci500209e.
-
(2014)
J. Chem. Inform. Model.
, vol.54
, pp. 2371-2379
-
-
Santos-Martins, D.1
Forli, S.2
Ramos, M.J.3
Olson, A.J.4
-
182
-
-
84880529288
-
Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments
-
Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R., and Sherman, W. (2013). Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 27, 221-234. doi:10.1007/s10822-013-9644-8.
-
(2013)
J. Comput. Aided Mol. Des.
, vol.27
, pp. 221-234
-
-
Sastry, G.M.1
Adzhigirey, M.2
Day, T.3
Annabhimoju, R.4
Sherman, W.5
-
183
-
-
0036467064
-
Entropy calculations on the molten globule state of a protein: Side-chain entropies of α-lactalbumin
-
Schäfer, H., Smith, L. J., Mark, A. E., and van Gunsteren, W. F. (2002). Entropy calculations on the molten globule state of a protein: Side-chain entropies of α-lactalbumin. Proteins Struct. Funct. Bioinform. 46, 215-224. doi:10.1002/prot.1166.
-
(2002)
Proteins Struct. Funct. Bioinform.
, vol.46
, pp. 215-224
-
-
Schäfer, H.1
Smith, L.J.2
Mark, A.E.3
Van Gunsteren, W.F.4
-
184
-
-
23844449940
-
Computer-based de novo design of drug-like molecules
-
Schneider, G., and Fechner, U. (2005). Computer-based de novo design of drug-like molecules. Nat. Rev. Drug Discov. 4, 649-663. doi:10.1038/nrd1799.
-
(2005)
Nat. Rev. Drug Discov.
, vol.4
, pp. 649-663
-
-
Schneider, G.1
Fechner, U.2
-
185
-
-
84923327259
-
DOCKTITE-a highly versatile step-by-step workflow for covalent docking and virtual screening in the molecular operating environment
-
Scholz, C., Knorr, S., Hamacher, K., and Schmidt, B. (2015). DOCKTITE-a highly versatile step-by-step workflow for covalent docking and virtual screening in the molecular operating environment. J. Chem. Inform. Model. 55, 398-406. doi:10.1021/ci500681r.
-
(2015)
J. Chem. Inform. Model.
, vol.55
, pp. 398-406
-
-
Scholz, C.1
Knorr, S.2
Hamacher, K.3
Schmidt, B.4
-
186
-
-
67349104587
-
Targeted scoring functions for virtual screening
-
Seifert, M. H. J. (2009). Targeted scoring functions for virtual screening. Drug Discov. Today 14, 562-569. doi:10.1016/j.drudis.2009.03.013.
-
(2009)
Drug Discov. Today
, vol.14
, pp. 562-569
-
-
Seifert, M.H.J.1
-
187
-
-
21144474350
-
Linear model selection by cross-validation
-
Shao, J. (1993). Linear model selection by cross-validation. J. Am. Stat. Assoc. 88, 486-494. doi:10.2307/2290328.
-
(1993)
J. Am. Stat. Assoc.
, vol.88
, pp. 486-494
-
-
Shao, J.1
-
188
-
-
33845491449
-
Interpreting steep dose-response curves in early inhibitor discovery
-
Shoichet, B. K. (2006). Interpreting steep dose-response curves in early inhibitor discovery. J. Med. Chem. 49, 7274-7277. doi:10.1021/jm061103g.
-
(2006)
J. Med. Chem.
, vol.49
, pp. 7274-7277
-
-
Shoichet, B.K.1
-
189
-
-
32844457567
-
Accurate calculation of hydration free energies using macroscopic solvent models
-
Sitkoff, D., Sharp, K. A., and Honig, B. (1994). Accurate calculation of hydration free energies using macroscopic solvent models. J. Phys. Chem. 98, 1978-1988doi:10.1021/j100058a043.
-
(1994)
J. Phys. Chem.
, vol.98
, pp. 1978-1988
-
-
Sitkoff, D.1
Sharp, K.A.2
Honig, B.3
-
190
-
-
84976347059
-
CSAR benchmark exercise 2013: Evaluation of results from a combined computational protein design, docking, and scoring/ranking challenge
-
Smith, R. D., Damm-Ganamet, K. L., Dunbar, J. B., Ahmed, A., Chinnaswamy, K., Delproposto, J. E., et al. (2016). CSAR benchmark exercise 2013: Evaluation of results from a combined computational protein design, docking, and scoring/ranking challenge. J. Chem. Inform. Model. 56, 1022-1031. doi:10.1021/acs.jcim.5b00387.
-
(2016)
J. Chem. Inform. Model.
, vol.56
, pp. 1022-1031
-
-
Smith, R.D.1
Damm-Ganamet, K.L.2
Dunbar, J.B.3
Ahmed, A.4
Chinnaswamy, K.5
Delproposto, J.E.6
-
191
-
-
84871717368
-
Scoring functions for protein-ligand interactions
-
, ed. H. Gohlke Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA
-
Sotriffer, C. (2012). "Scoring functions for protein-ligand interactions, " in Protein-Ligand Interactions, ed. H. Gohlke (Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA), 237-263.
-
(2012)
Protein-Ligand Interactions
, pp. 237-263
-
-
Sotriffer, C.1
-
192
-
-
84883237021
-
The challenge of affinity prediction: Scoring functions for structure-based virtual screening
-
ed. C. Sotriffer Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA
-
Sotriffer, C., and Matter, H. (2011). "The challenge of affinity prediction: Scoring functions for structure-based virtual screening, " in Methods and Principles in Medicinal Chemistry, ed. C. Sotriffer (Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA), 177-221.
-
(2011)
Methods and Principles in Medicinal Chemistry
, pp. 177-221
-
-
Sotriffer, C.1
Matter, H.2
-
193
-
-
52249113723
-
SFCscore: Scoring functions for affinity prediction of protein-ligand complexes
-
Sotriffer, C. A., Sanschagrin, P., Matter, H., and Klebe, G. (2008). SFCscore: Scoring functions for affinity prediction of protein-ligand complexes. Proteins 73, 395-419. doi:10.1002/prot.22058.
-
(2008)
Proteins
, vol.73
, pp. 395-419
-
-
Sotriffer, C.A.1
Sanschagrin, P.2
Matter, H.3
Klebe, G.4
-
194
-
-
85033356319
-
DMM-PBSA: A new HADDOCK scoring function for protein-peptide docking
-
Spiliotopoulos, D., Kastritis, P. L., Melquiond, A. S. J., Bonvin, A. M. J. J., Musco, G., Rocchia, W., et al. (2016). dMM-PBSA: A new HADDOCK scoring function for protein-peptide docking. Front. Mol. Biosci. 3:46. doi:10.3389/fmolb.2016.00046.
-
(2016)
Front. Mol. Biosci.
, vol.3
, pp. 46
-
-
Spiliotopoulos, D.1
Kastritis, P.L.2
Melquiond, A.S.J.3
Bonvin, A.M.J.J.4
Musco, G.5
Rocchia, W.6
-
195
-
-
84939813976
-
Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description
-
Spyrakis, F., and Cavasotto, C. N. (2015). Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description. Arch. Biochem. Biophys. 583, 105-119. doi:10.1016/j.abb.2015.08.002.
-
(2015)
Arch. Biochem. Biophys.
, vol.583
, pp. 105-119
-
-
Spyrakis, F.1
Cavasotto, C.N.2
-
196
-
-
0344778061
-
Semianalytical treatment of solvation for molecular mechanics and dynamics
-
Still, W. C., Tempczyk, A., Hawley, R. C., and Hendrickson, T. (1990). Semianalytical treatment of solvation for molecular mechanics and dynamics. J. Am. Chem. Soc. 112, 6127-6129. doi:10.1021/ja00172a038.
-
(1990)
J. Am. Chem. Soc.
, vol.112
, pp. 6127-6129
-
-
Still, W.C.1
Tempczyk, A.2
Hawley, R.C.3
Hendrickson, T.4
-
197
-
-
41349113136
-
Editorial: Special issue on "evaluation of computational methods
-
Stouch, T. (2008). Editorial: Special issue on "evaluation of computational methods." J. Comput. Aided Mol. Des. 22:131. doi:10.1007/s10822-008-9197-4.
-
(2008)
J. Comput. Aided Mol. Des.
, vol.22
, pp. 131
-
-
Stouch, T.1
-
198
-
-
84904438047
-
Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set
-
Sun, H., Li, Y., Tian, S., Xu, L., and Hou, T. (2014). Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys. Chem. Chem. Phys. 16, 16719-16729. doi:10.1039/c4cp01388c.
-
(2014)
Phys. Chem. Chem. Phys.
, vol.16
, pp. 16719-16729
-
-
Sun, H.1
Li, Y.2
Tian, S.3
Xu, L.4
Hou, T.5
-
200
-
-
34247262566
-
Supervised consensus scoring for docking and virtual screening
-
Teramoto, R., and Fukunishi, H. (2007). Supervised consensus scoring for docking and virtual screening. J. Chem. Inform. Model. 47, 526-534. doi:10.1021/ci6004993.
-
(2007)
J. Chem. Inform. Model.
, vol.47
, pp. 526-534
-
-
Teramoto, R.1
Fukunishi, H.2
-
201
-
-
0035811458
-
A new concept for multidimensional selection of ligand conformations (multiselect) and multidimensional scoring (multiscore) of protein-ligand binding affinities
-
Terp, G. E., Johansen, B. N., Christensen, I. T., and Jørgensen, F. S. (2001). A new concept for multidimensional selection of ligand conformations (multiselect) and multidimensional scoring (multiscore) of protein-ligand binding affinities. J. Med. Chem. 44, 2333-2343. doi:10.1021/jm001090l.
-
(2001)
J. Med. Chem.
, vol.44
, pp. 2333-2343
-
-
Terp, G.E.1
Johansen, B.N.2
Christensen, I.T.3
Jørgensen, F.S.4
-
202
-
-
0031302358
-
Flexible protein-ligand docking by global energy optimization in internal coordinates
-
Totrov, M., and Abagyan, R. (1997). Flexible protein-ligand docking by global energy optimization in internal coordinates. Proteins Suppl. 1, 215-220.
-
(1997)
Proteins Suppl
, vol.1
, pp. 215-220
-
-
Totrov, M.1
Abagyan, R.2
-
204
-
-
85042878150
-
Rapid measurement of inhibitor binding kinetics by isothermal titration calorimetry
-
Trani, J. M. D., Cesco, S. D., O'Leary, R., Plescia, J., Nascimento, C. J. do, Moitessier, N., et al. (2018). Rapid measurement of inhibitor binding kinetics by isothermal titration calorimetry. Nat. Commun. 9:893. doi:10.1038/s41467-018-03263-3.
-
(2018)
Nat. Commun.
, vol.9
, pp. 893
-
-
Trani, J.M.D.1
Cesco, S.D.2
O'Leary, R.3
Plescia, J.4
Nascimento, C.J.D.5
Moitessier, N.6
-
205
-
-
76149120388
-
AutoDock vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading
-
Trott, O., and Olson, A. J. (2010). AutoDock vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J. Comput. Chem. 31, 455-461. doi:10.1002/jcc.21334.
-
(2010)
J. Comput. Chem.
, vol.31
, pp. 455-461
-
-
Trott, O.1
Olson, A.J.2
-
206
-
-
84855460321
-
Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: Limitations of current computational approaches
-
Tuffery, P., and Derreumaux, P. (2012). Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: Limitations of current computational approaches. J. R. Soc. Interface 9, 20-33. doi:10.1098/rsif.2011.0584.
-
(2012)
J. R. Soc. Interface
, vol.9
, pp. 20-33
-
-
Tuffery, P.1
Derreumaux, P.2
-
207
-
-
85050906554
-
The taxonomy of covalent inhibitors
-
Mosc
-
Tuley, A., and Fast, W. (2018). The taxonomy of covalent inhibitors. Biochemistry (Mosc.) 57, 3326-3337. doi:10.1021/acs.biochem.8b00315.
-
(2018)
Biochemistry
, vol.57
, pp. 3326-3337
-
-
Tuley, A.1
Fast, W.2
-
208
-
-
85043348813
-
Recent updates on computer-aided drug discovery: Time for a paradigm shift
-
Usha, T., Shanmugarajan, D., Goyal, A. K., Kumar, C. S., and Middha, S. K. (2017). Recent updates on computer-aided drug discovery: Time for a paradigm shift. Curr. Top. Med. Chem. 17, 3296-3307. doi:10.2174/1568026618666180101163651.
-
(2017)
Curr. Top. Med. Chem.
, vol.17
, pp. 3296-3307
-
-
Usha, T.1
Shanmugarajan, D.2
Goyal, A.K.3
Kumar, C.S.4
Middha, S.K.5
-
209
-
-
84959457637
-
The HADDOCK2.2 web server: User-friendly integrative modeling of biomolecular complexes
-
van Zundert, G. C. P., Rodrigues, J. P. G. L. M., Trellet, M., Schmitz, C., Kastritis, P. L., Karaca, E., et al. (2016). The HADDOCK2.2 web server: User-friendly integrative modeling of biomolecular complexes. J. Mol. Biol. 428, 720-725. doi:10.1016/j.jmb.2015.09.014.
-
(2016)
J. Mol. Biol.
, vol.428
, pp. 720-725
-
-
Van Zundert, G.C.P.1
Rodrigues, J.P.G.L.M.2
Trellet, M.3
Schmitz, C.4
Kastritis, P.L.5
Karaca, E.6
-
210
-
-
26444588137
-
DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction
-
Velec, H. F. G., Gohlke, H., and Klebe, G. (2005). DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J. Med. Chem. 48, 6296-6303. doi:10.1021/jm050436v.
-
(2005)
J. Med. Chem.
, vol.48
, pp. 6296-6303
-
-
Velec, H.F.G.1
Gohlke, H.2
Klebe, G.3
-
211
-
-
2942721004
-
Virtual screening using protein-ligand docking: Avoiding artificial enrichment
-
Verdonk, M. L., Berdini, V., Hartshorn, M. J., Mooij, W. T. M., Murray, C. W., Taylor, R. D., et al. (2004). Virtual screening using protein-ligand docking: Avoiding artificial enrichment. J. Chem. Inform. Model. 44, 793-806. doi:10.1021/ci034289q.
-
(2004)
J. Chem. Inform. Model.
, vol.44
, pp. 793-806
-
-
Verdonk, M.L.1
Berdini, V.2
Hartshorn, M.J.3
Mooij, W.T.M.4
Murray, C.W.5
Taylor, R.D.6
-
212
-
-
26444586139
-
Modeling water molecules in protein-ligand docking using GOLD
-
Verdonk, M. L., Chessari, G., Cole, J. C., Hartshorn, M. J., Murray, C. W., Nissink, J. W. M., et al. (2005). Modeling water molecules in protein-ligand docking using GOLD. J. Med. Chem. 48, 6504-6515. doi:10.1021/jm050543p.
-
(2005)
J. Med. Chem.
, vol.48
, pp. 6504-6515
-
-
Verdonk, M.L.1
Chessari, G.2
Cole, J.C.3
Hartshorn, M.J.4
Murray, C.W.5
Nissink, J.W.M.6
-
213
-
-
71249128422
-
Structure-based virtual ligand screening: Recent success stories
-
Villoutreix, B., Eudes, R., and Miteva, M. (2009). Structure-based virtual ligand screening: Recent success stories. Comb. Chem. High Throughput Screen. 12, 1000-1016. doi:10.2174/138620709789824682.
-
(2009)
Comb. Chem. High Throughput Screen
, vol.12
, pp. 1000-1016
-
-
Villoutreix, B.1
Eudes, R.2
Miteva, M.3
-
214
-
-
80054933039
-
DEKOIS: Demanding evaluation kits for objective in silico screening - A versatile tool for benchmarking docking programs and scoring functions
-
Vogel, S. M., Bauer, M. R., and Boeckler, F. M. (2011). DEKOIS: Demanding evaluation kits for objective in silico screening - A versatile tool for benchmarking docking programs and scoring functions. J. Chem. Inf. Model. 51, 2650-2665. doi:10.1021/ci2001549.
-
(2011)
J. Chem. Inf. Model.
, vol.51
, pp. 2650-2665
-
-
Vogel, S.M.1
Bauer, M.R.2
Boeckler, F.M.3
-
216
-
-
85000454204
-
Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest
-
Wang, C., and Zhang, Y. (2017). Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. J. Comput. Chem. 38, 169-177. doi:10.1002/jcc.24667.
-
(2017)
J. Comput. Chem.
, vol.38
, pp. 169-177
-
-
Wang, C.1
Zhang, Y.2
-
217
-
-
80054928964
-
Robust scoring functions for protein-ligand interactions with quantum chemical charge models
-
Wang, J.-C., Lin, J.-H., Chen, C.-M., Perryman, A. L., and Olson, A. J. (2011). Robust scoring functions for protein-ligand interactions with quantum chemical charge models. J. Chem. Inform. Model. 51, 2528-2537. doi:10.1021/ci200220v.
-
(2011)
J. Chem. Inform. Model.
, vol.51
, pp. 2528-2537
-
-
Wang, J.-C.1
Lin, J.-H.2
Chen, C.-M.3
Perryman, A.L.4
Olson, A.J.5
-
218
-
-
0036022960
-
Further development and validation of empirical scoring functions for structure-based binding affinity prediction
-
Wang, R., Lai, L., and Wang, S. (2002). Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J. Comput. Aided Mol. Des. 16, 11-26.
-
(2002)
J. Comput. Aided Mol. Des.
, vol.16
, pp. 11-26
-
-
Wang, R.1
Lai, L.2
Wang, S.3
-
219
-
-
0001704085
-
SCORE: A new empirical method for estimating the binding affinity of a protein-ligand complex
-
Wang, R., Liu, L., Lai, L., and Tang, Y. (1998). SCORE: A new empirical method for estimating the binding affinity of a protein-ligand complex. J. Mol. Model. 4, 379-394. doi:10.1007/s008940050096.
-
(1998)
J. Mol. Model.
, vol.4
, pp. 379-394
-
-
Wang, R.1
Liu, L.2
Lai, L.3
Tang, Y.4
-
220
-
-
0037763817
-
Comparative evaluation of 11 scoring functions for molecular docking
-
Wang, R., Lu, Y., and Wang, S. (2003). Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. 46, 2287-2303. doi:10.1021/jm0203783.
-
(2003)
J. Med. Chem.
, vol.46
, pp. 2287-2303
-
-
Wang, R.1
Lu, Y.2
Wang, S.3
-
221
-
-
0035438402
-
How does consensus scoring work for virtual library screening? An idealized computer experiment
-
Wang, R., and Wang, S. (2001). How does consensus scoring work for virtual library screening? An idealized computer experiment. J. Chem. Inform. Comput. Sci. 41, 1422-1426.
-
(2001)
J. Chem. Inform. Comput. Sci.
, vol.41
, pp. 1422-1426
-
-
Wang, R.1
Wang, S.2
-
222
-
-
84925496993
-
A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach
-
Wang, Y., Guo, Y., Kuang, Q., Pu, X., Ji, Y., Zhang, Z., et al. (2015). A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach. J. Comput. Aided Mol. Des. 29, 349-360. doi:10.1007/s10822-014-9827-y.
-
(2015)
J. Comput. Aided Mol. Des.
, vol.29
, pp. 349-360
-
-
Wang, Y.1
Guo, Y.2
Kuang, Q.3
Pu, X.4
Ji, Y.5
Zhang, Z.6
-
223
-
-
0036382728
-
A model binding site for testing scoring functions in molecular docking
-
Wei, B. Q., Baase, W. A., Weaver, L. H., Matthews, B. W., and Shoichet, B. K. (2002). A model binding site for testing scoring functions in molecular docking. J. Mol. Biol. 322, 339-355.
-
(2002)
J. Mol. Biol.
, vol.322
, pp. 339-355
-
-
Wei, B.Q.1
Baase, W.A.2
Weaver, L.H.3
Matthews, B.W.4
Shoichet, B.K.5
-
224
-
-
77958565592
-
Binding energy landscape analysis helps to discriminate true hits from high-scoring decoys in virtual screening
-
Wei, D., Zheng, H., Su, N., Deng, M., and Lai, L. (2010). Binding energy landscape analysis helps to discriminate true hits from high-scoring decoys in virtual screening. J. Chem. Inform. Model. 50, 1855-1864. doi:10.1021/ci900463u.
-
(2010)
J. Chem. Inform. Model.
, vol.50
, pp. 1855-1864
-
-
Wei, D.1
Zheng, H.2
Su, N.3
Deng, M.4
Lai, L.5
-
225
-
-
0033451033
-
Estimating binding constants - The hydrophobic effect and cooperativity
-
Williams, D. H., and Bardsley, B. (1999). Estimating binding constants - The hydrophobic effect and cooperativity. Perspect. Drug Discov. Des. 17, 43-59. doi:10.1023/A:1008770523049.
-
(1999)
Perspect. Drug Discov. Des.
, vol.17
, pp. 43-59
-
-
Williams, D.H.1
Bardsley, B.2
-
226
-
-
85027440798
-
Performance of machine-learning scoring functions in structure-based virtual screening
-
Wójcikowski, M., Ballester, P. J., and Siedlecki, P. (2017). Performance of machine-learning scoring functions in structure-based virtual screening. Sci. Rep. 7:46710. doi:10.1038/srep46710.
-
(2017)
Sci. Rep.
, vol.7
, pp. 46710
-
-
Wójcikowski, M.1
Ballester, P.J.2
Siedlecki, P.3
-
227
-
-
23844555629
-
Consensus scoring criteria for improving enrichment in virtual screening
-
Yang, J.-M., Chen, Y.-F., Shen, T.-W., Kristal, B. S., and Hsu, D. F. (2005). Consensus scoring criteria for improving enrichment in virtual screening. J. Chem. Inform. Model. 45, 1134-1146. doi:10.1021/ci050034w.
-
(2005)
J. Chem. Inform. Model.
, vol.45
, pp. 1134-1146
-
-
Yang, J.-M.1
Chen, Y.-F.2
Shen, T.-W.3
Kristal, B.S.4
Hsu, D.F.5
-
228
-
-
84874299846
-
Approaches to efficiently estimate solvation and explicit water energetics in ligand binding: The use of WaterMap
-
Yang, Y., Lightstone, F. C., and Wong, S. E. (2013). Approaches to efficiently estimate solvation and explicit water energetics in ligand binding: The use of WaterMap. Exp. Opin. Drug Discov. 8, 277-287. doi:10.1517/17460441.2013.749853.
-
(2013)
Exp. Opin. Drug Discov.
, vol.8
, pp. 277-287
-
-
Yang, Y.1
Lightstone, F.C.2
Wong, S.E.3
-
229
-
-
84938078670
-
A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions
-
Yang, Z., Liu, Y., Chen, Z., Xu, Z., Shi, J., Chen, K., et al. (2015). A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions. J. Mol. Model. 21:138. doi:10.1007/s00894-015-2681-6.
-
(2015)
J. Mol. Model.
, vol.21
, pp. 138
-
-
Yang, Z.1
Liu, Y.2
Chen, Z.3
Xu, Z.4
Shi, J.5
Chen, K.6
-
230
-
-
84983643559
-
Prospects of applying enhanced semi-empirical QM methods for 2101 virtual drug design
-
Yilmazer, N. D., and Korth, M. (2016). Prospects of applying enhanced semi-empirical QM methods for 2101 virtual drug design. Curr. Med. Chem. 23, 2101-2111.
-
(2016)
Curr. Med. Chem.
, vol.23
, pp. 2101-2111
-
-
Yilmazer, N.D.1
Korth, M.2
-
231
-
-
84941075640
-
Improvements, trends, and new ideas in molecular docking: 2012-2013 in review: Improvements, trends, and new ideas in molecular docking
-
Yuriev, E., Holien, J., and Ramsland, P. A. (2015). Improvements, trends, and new ideas in molecular docking: 2012-2013 in review: Improvements, trends, and new ideas in molecular docking. J. Mol. Recognit. 28, 581-604. doi:10.1002/jmr.2471.
-
(2015)
J. Mol. Recognit.
, vol.28
, pp. 581-604
-
-
Yuriev, E.1
Holien, J.2
Ramsland, P.A.3
-
232
-
-
84875480305
-
Latest developments in molecular docking: 2010-2011 in review
-
Yuriev, E., and Ramsland, P. A. (2013). Latest developments in molecular docking: 2010-2011 in review. J. Mol. Recognit. JMR 26, 215-239. doi:10.1002/jmr.2266.
-
(2013)
J. Mol. Recognit. JMR
, vol.26
, pp. 215-239
-
-
Yuriev, E.1
Ramsland, P.A.2
-
233
-
-
85028803040
-
From machine learning to deep learning: Progress in machine intelligence for rational drug discovery
-
Zhang, L., Tan, J., Han, D., and Zhu, H. (2017). From machine learning to deep learning: Progress in machine intelligence for rational drug discovery. Drug Discov. Today 22, 1680-1685. doi:10.1016/j.drudis.2017.08.010.
-
(2017)
Drug Discov. Today
, vol.22
, pp. 1680-1685
-
-
Zhang, L.1
Tan, J.2
Han, D.3
Zhu, H.4
-
234
-
-
85019550196
-
A comprehensive docking and MM/GBSA rescoring study of ligand recognition upon binding antithrombin
-
Zhang, X., Perez-Sanchez, H., and Lightstone, F. C. (2017). A comprehensive docking and MM/GBSA rescoring study of ligand recognition upon binding antithrombin. Curr. Top. Med. Chem. 17, 1631-1639. doi:10.2174/1568026616666161117112604.
-
(2017)
Curr. Top. Med. Chem.
, vol.17
, pp. 1631-1639
-
-
Zhang, X.1
Perez-Sanchez, H.2
Lightstone, F.C.3
-
235
-
-
79959765741
-
Ligand identification scoring algorithm (LISA)
-
Zheng, Z., and Merz, K. M. (2011). Ligand identification scoring algorithm (LISA). J. Chem. Inform. Model. 51, 1296-1306. doi:10.1021/ci2000665.
-
(2011)
J. Chem. Inform. Model.
, vol.51
, pp. 1296-1306
-
-
Zheng, Z.1
Merz, K.M.2
-
236
-
-
84904966114
-
Docking covalent inhibitors: A parameter free approach to pose prediction and scoring
-
Zhu, K., Borrelli, K. W., Greenwood, J. R., Day, T., Abel, R., Farid, R. S., et al. (2014). Docking covalent inhibitors: A parameter free approach to pose prediction and scoring. J. Chem. Inform. Model. 54, 1932-1940. doi:10.1021/ci500118s.
-
(2014)
J. Chem. Inform. Model.
, vol.54
, pp. 1932-1940
-
-
Zhu, K.1
Borrelli, K.W.2
Greenwood, J.R.3
Day, T.4
Abel, R.5
Farid, R.S.6
-
237
-
-
84883250593
-
SFCscore RF: A random forest-based scoring function for improved affinity prediction of proteinligand complexes
-
Zilian, D., and Sotriffer, C. A. (2013). SFCscore RF: A random forest-based scoring function for improved affinity prediction of proteinligand complexes. J. Chem. Inf. Model. 53, 1923-1933. doi:10.1021/ci400120b.
-
(2013)
J. Chem. Inf. Model.
, vol.53
, pp. 1923-1933
-
-
Zilian, D.1
Sotriffer, C.A.2
-
238
-
-
84925434864
-
Evaluating the potential of halogen bonding in molecular design: Automated scaffold decoration using the new scoring function XBScore
-
Zimmermann, M. O., Lange, A., and Boeckler, F. M. (2015). Evaluating the potential of halogen bonding in molecular design: Automated scaffold decoration using the new scoring function XBScore. J. Chem. Inform. Model. 55, 687-699. doi:10.1021/ci5007118.
-
(2015)
J. Chem. Inform. Model.
, vol.55
, pp. 687-699
-
-
Zimmermann, M.O.1
Lange, A.2
Boeckler, F.M.3
-
239
-
-
0033536456
-
Inclusion of solvation in ligand binding free energy calculations using the generalized-born model
-
Zou, X., and Kuntz, I. D. (1999). Inclusion of solvation in ligand binding free energy calculations using the generalized-born model. J. Am. Chem. Soc. 121, 8033-8043. doi:10.1021/ja984102p.
-
(1999)
J. Am. Chem. Soc.
, vol.121
, pp. 8033-8043
-
-
Zou, X.1
Kuntz, I.D.2
|