-
1
-
-
33947340384
-
Reaction rates and indicator acidities
-
Hammett LP. Reaction rates and indicator acidities. Chem Rev 1935, 16:67-79.
-
(1935)
Chem Rev
, vol.16
, pp. 67-79
-
-
Hammett, L.P.1
-
2
-
-
0040914011
-
p-σ-π Analysis. A method for the correlation of biological activity and chemical structure
-
Hansch C, Fujita T. p-σ-π Analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 1964, 86:1616-1626.
-
(1964)
J Am Chem Soc
, vol.86
, pp. 1616-1626
-
-
Hansch, C.1
Fujita, T.2
-
3
-
-
33751553380
-
New QSAR techniques eyed for environmental assessments
-
Borman S. New QSAR techniques eyed for environmental assessments. Chem Eng News 1990, 19:20-23.
-
(1990)
Chem Eng News
, vol.19
, pp. 20-23
-
-
Borman, S.1
-
4
-
-
70350284077
-
Pattern recognition in chemical research
-
Klopfenstein CE, Wilkins CL, eds. Academic Press: New York
-
Kowalski BR. Pattern recognition in chemical research. In: Klopfenstein CE, Wilkins CL, eds. Computers in Chemical and Biochemical Research, vol. 2. Academic Press: New York; 1974, 1-76.
-
(1974)
Computers in Chemical and Biochemical Research
, vol.2
, pp. 1-76
-
-
Kowalski, B.R.1
-
5
-
-
84880542260
-
Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules
-
Lusci A, Pollastri G, Baldi P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inf Model 2013, 53:1563-1575.
-
(2013)
J Chem Inf Model
, vol.53
, pp. 1563-1575
-
-
Lusci, A.1
Pollastri, G.2
Baldi, P.3
-
8
-
-
0028466540
-
Comparison of automatic three-dimensional model builders using 639 X-ray structures
-
Sadowski J, Gasteiger J, Klebe G. Comparison of automatic three-dimensional model builders using 639 X-ray structures. J Chem Inf Comput Sci 1994, 34:1000-1008.
-
(1994)
J Chem Inf Comput Sci
, vol.34
, pp. 1000-1008
-
-
Sadowski, J.1
Gasteiger, J.2
Klebe, G.3
-
9
-
-
0036740917
-
Why do we need so many chemical similarity search methods?
-
Sheridan RP, Kearsley SK. Why do we need so many chemical similarity search methods? Drug Discov Today 2002, 7:903-911.
-
(2002)
Drug Discov Today
, vol.7
, pp. 903-911
-
-
Sheridan, R.P.1
Kearsley, S.K.2
-
10
-
-
78650689245
-
Comprehensive comparison of ligand-based virtual screening tools against the DUD data set reveals limitations of current 3D methods
-
Venkatraman V, Perez-Nueno VI, Mavridis L, Ritchie DW. Comprehensive comparison of ligand-based virtual screening tools against the DUD data set reveals limitations of current 3D methods. J Chem Inf Model 2010, 50:2079-2093.
-
(2010)
J Chem Inf Model
, vol.50
, pp. 2079-2093
-
-
Venkatraman, V.1
Perez-Nueno, V.I.2
Mavridis, L.3
Ritchie, D.W.4
-
12
-
-
0000378338
-
Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle
-
Zheng W, Tropsha A. Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. J Chem Inf Model 2000, 40:185-194.
-
(2000)
J Chem Inf Model
, vol.40
, pp. 185-194
-
-
Zheng, W.1
Tropsha, A.2
-
13
-
-
57649213892
-
Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction
-
O'Boyle NM, Palmer DS, Nigsch F, Mitchell JBO. Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction. Chem Cent J 2008, 2:21.
-
(2008)
Chem Cent J
, vol.2
, pp. 21
-
-
O'Boyle, N.M.1
Palmer, D.S.2
Nigsch, F.3
Mitchell, J.B.O.4
-
14
-
-
40449141013
-
What is principal component analysis?
-
Ringner M. What is principal component analysis? Nat Biotech 2008, 26:303-304.
-
(2008)
Nat Biotech
, vol.26
, pp. 303-304
-
-
Ringner, M.1
-
15
-
-
33746931581
-
On outliers and activity cliffs-why QSAR often disappoints
-
Maggiora GM. On outliers and activity cliffs-why QSAR often disappoints. J Chem Inf Model 2006, 46:1535.
-
(2006)
J Chem Inf Model
, vol.46
, pp. 1535
-
-
Maggiora, G.M.1
-
16
-
-
0035263415
-
Prediction of drug solubility by the general solubility equation (GSE)
-
Ran Y, Yalkowsky SH. Prediction of drug solubility by the general solubility equation (GSE). J Chem Inf Comput Sci 2001, 41:354-357.
-
(2001)
J Chem Inf Comput Sci
, vol.41
, pp. 354-357
-
-
Ran, Y.1
Yalkowsky, S.H.2
-
17
-
-
42549147181
-
Predicting intrinsic aqueous solubility by a thermodynamic cycle
-
Palmer DS, Llinas A, Morao I, Day GM, Goodman JM, Glen RC, Mitchell JBO. Predicting intrinsic aqueous solubility by a thermodynamic cycle. Mol Pharm 2008, 5:266-279.
-
(2008)
Mol Pharm
, vol.5
, pp. 266-279
-
-
Palmer, D.S.1
Llinas, A.2
Morao, I.3
Day, G.M.4
Goodman, J.M.5
Glen, R.C.6
Mitchell, J.B.O.7
-
18
-
-
72949117724
-
In silico prediction of aqueous solubility: the solubility challenge
-
Hewitt M, Cronin MTD, Enoch SJ, Madden JC, Roberts DW, Dearden JC. In silico prediction of aqueous solubility: the solubility challenge. J Chem Inf Model 2009, 49:2572-2587.
-
(2009)
J Chem Inf Model
, vol.49
, pp. 2572-2587
-
-
Hewitt, M.1
Cronin, M.T.D.2
Enoch, S.J.3
Madden, J.C.4
Roberts, D.W.5
Dearden, J.C.6
-
19
-
-
37049075162
-
Quantitative structure-sublimation enthalpy relationship studied by neural networks, theoretical crystal packing calculations and nonlinear regression analysis
-
Charlton MH, Docherty R, Hutchings MG. Quantitative structure-sublimation enthalpy relationship studied by neural networks, theoretical crystal packing calculations and nonlinear regression analysis. J Chem Soc Perkin Trans 1995, 2:2023-2030.
-
(1995)
J Chem Soc Perkin Trans
, vol.2
, pp. 2023-2030
-
-
Charlton, M.H.1
Docherty, R.2
Hutchings, M.G.3
-
20
-
-
2442648065
-
Can we predict lattice energy from molecular structure?
-
Ouvrard C, Mitchell JBO. Can we predict lattice energy from molecular structure? Acta Cryst B 2003, 59:676-685.
-
(2003)
Acta Cryst B
, vol.59
, pp. 676-685
-
-
Ouvrard, C.1
Mitchell, J.B.O.2
-
21
-
-
84873023995
-
Capturing the crystal: prediction of enthalpy of sublimation, crystal lattice energy, and melting points of organic compounds
-
Salahinejad M, Le TC, Winkler DA. Capturing the crystal: prediction of enthalpy of sublimation, crystal lattice energy, and melting points of organic compounds. J Chem Inf Model 2013, 53:223-229.
-
(2013)
J Chem Inf Model
, vol.53
, pp. 223-229
-
-
Salahinejad, M.1
Le, T.C.2
Winkler, D.A.3
-
22
-
-
3943074508
-
Electrical synapses in the mammalian brain
-
Connors BW, Long MA. Electrical synapses in the mammalian brain. Annu Rev Neurosci 2004, 27:393-418.
-
(2004)
Annu Rev Neurosci
, vol.27
, pp. 393-418
-
-
Connors, B.W.1
Long, M.A.2
-
23
-
-
33750982700
-
Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods
-
Li H, Ung C, Yap C, Xue Y, Li Z, Chen Y. Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods. J Mol Graph Model 2006, 25:313-323.
-
(2006)
J Mol Graph Model
, vol.25
, pp. 313-323
-
-
Li, H.1
Ung, C.2
Yap, C.3
Xue, Y.4
Li, Z.5
Chen, Y.6
-
24
-
-
0031434910
-
Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations
-
So SS, Karplus M. Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations. J Med Chem 1997, 40:4347-4359.
-
(1997)
J Med Chem
, vol.40
, pp. 4347-4359
-
-
So, S.S.1
Karplus, M.2
-
25
-
-
20444489197
-
Classifying "kinase inhibitor-likeness" by using machine-learning methods
-
Briem H, Günther J. Classifying "kinase inhibitor-likeness" by using machine-learning methods. ChemBioChem Eur J Chem Biol 2005, 6:558-566.
-
(2005)
ChemBioChem Eur J Chem Biol
, vol.6
, pp. 558-566
-
-
Briem, H.1
Günther, J.2
-
26
-
-
52649109749
-
Classification models for hERG inhibitors by counter-propagation neural networks
-
Thai KMM, Ecker GF. Classification models for hERG inhibitors by counter-propagation neural networks. Chem Biol Drug Des 2008, 72:279-289.
-
(2008)
Chem Biol Drug Des
, vol.72
, pp. 279-289
-
-
Thai, K.M.M.1
Ecker, G.F.2
-
27
-
-
0034218972
-
Use of statistical and neural net approaches in predicting toxicity of chemicals
-
Basak SC, Grunwald GD, Gute BD, Balasubramanian K, Opitz D. Use of statistical and neural net approaches in predicting toxicity of chemicals. J Chem Inf Comput Sci 2000, 40:885-890.
-
(2000)
J Chem Inf Comput Sci
, vol.40
, pp. 885-890
-
-
Basak, S.C.1
Grunwald, G.D.2
Gute, B.D.3
Balasubramanian, K.4
Opitz, D.5
-
28
-
-
33644616650
-
Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods
-
Yap CW, Li ZR, Chen YZ. Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods. J Mol Graph Model 2006, 24:383-395.
-
(2006)
J Mol Graph Model
, vol.24
, pp. 383-395
-
-
Yap, C.W.1
Li, Z.R.2
Chen, Y.Z.3
-
29
-
-
69549086566
-
pKa Prediction from quantum chemical topology descriptors
-
Harding AP, Wedge DC, Popelier PLA. pKa Prediction from quantum chemical topology descriptors. J Chem Inf Model 2009, 49:1914-1924.
-
(2009)
J Chem Inf Model
, vol.49
, pp. 1914-1924
-
-
Harding, A.P.1
Wedge, D.C.2
Popelier, P.L.A.3
-
30
-
-
39449107168
-
Prediction of melting points of organic compounds using extreme learning machines
-
Bhat AU, Merchant SS, Bhagwat SS. Prediction of melting points of organic compounds using extreme learning machines. Ind Eng Chem Res 2008, 47:920-925.
-
(2008)
Ind Eng Chem Res
, vol.47
, pp. 920-925
-
-
Bhat, A.U.1
Merchant, S.S.2
Bhagwat, S.S.3
-
31
-
-
33746322753
-
An improved structure-property model for predicting melting-point temperatures
-
Godavarthy SS, Robinson RL, Gasem KAM. An improved structure-property model for predicting melting-point temperatures. Ind Eng Chem Res 2006, 45:5117-5126.
-
(2006)
Ind Eng Chem Res
, vol.45
, pp. 5117-5126
-
-
Godavarthy, S.S.1
Robinson, R.L.2
Gasem, K.A.M.3
-
32
-
-
84866731321
-
Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks
-
Erić S, Kalinić M, Popović A, Zloh M, Kuzmanovski I. Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks. Int J Pharm 2012, 437:232-241.
-
(2012)
Int J Pharm
, vol.437
, pp. 232-241
-
-
Erić, S.1
Kalinić, M.2
Popović, A.3
Zloh, M.4
Kuzmanovski, I.5
-
33
-
-
77955557553
-
Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses
-
Louis B, Agrawal VK, Khadikar PV. Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses. Eur J Med Chem 2010, 45:4018-4025.
-
(2010)
Eur J Med Chem
, vol.45
, pp. 4018-4025
-
-
Louis, B.1
Agrawal, V.K.2
Khadikar, P.V.3
-
34
-
-
84867720412
-
Improving neural networks by preventing co-adaptation of feature detectors
-
Available at:
-
Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. Improving neural networks by preventing co-adaptation of feature detectors. 2012. Available at: http://arxiv.org/abs/1207.0580.
-
(2012)
-
-
Hinton, G.E.1
Srivastava, N.2
Krizhevsky, A.3
Sutskever, I.4
Salakhutdinov, R.R.5
-
36
-
-
0141733943
-
Vox populi
-
Galton F. Vox populi. Nature 1907, 75:450-451.
-
(1907)
Nature
, vol.75
, pp. 450-451
-
-
Galton, F.1
-
37
-
-
0033576680
-
Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins
-
Charifson PS, Corkery JJ, Murcko MA, Walters WP. Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 1999, 42:5100-5109.
-
(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
-
38
-
-
0035478854
-
Random Forests
-
Breiman L. Random Forests. Mach Learn 2001, 45:5-32.
-
(2001)
Mach Learn
, vol.45
, pp. 5-32
-
-
Breiman, L.1
-
39
-
-
0345548657
-
Random forest: a classification and regression tool for compound classification and QSAR modeling
-
Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 2003, 43:1947-1958.
-
(2003)
J Chem Inf Comput Sci
, vol.43
, pp. 1947-1958
-
-
Svetnik, V.1
Liaw, A.2
Tong, C.3
Culberson, J.C.4
Sheridan, R.P.5
Feuston, B.P.6
-
40
-
-
3943113604
-
Theoretical comparison between the Gini Index and Information Gain criteria
-
Raileanu LE, Stoffel K. Theoretical comparison between the Gini Index and Information Gain criteria. Ann Math Artif Intell 2004, 41:77-93.
-
(2004)
Ann Math Artif Intell
, vol.41
, pp. 77-93
-
-
Raileanu, L.E.1
Stoffel, K.2
-
41
-
-
43349090139
-
A novel hybrid ultrafast shape descriptor method for use in virtual screening
-
Cannon EO, Nigsch F, Mitchell JBO. A novel hybrid ultrafast shape descriptor method for use in virtual screening. Chem Cent J 2008, 2:3.
-
(2008)
Chem Cent J
, vol.2
, pp. 3
-
-
Cannon, E.O.1
Nigsch, F.2
Mitchell, J.B.O.3
-
42
-
-
33845782504
-
Chemoinformatics-based classification of prohibited substances employed for doping in sport
-
Cannon EO, Bender A, Palmer DS, Mitchell JBO. Chemoinformatics-based classification of prohibited substances employed for doping in sport. J Chem Inf Model 2006, 46:2369-2380.
-
(2006)
J Chem Inf Model
, vol.46
, pp. 2369-2380
-
-
Cannon, E.O.1
Bender, A.2
Palmer, D.S.3
Mitchell, J.B.O.4
-
43
-
-
79960570801
-
Interpretation of QSAR models based on random forest methods
-
Kuz'min VE, Polishchuk PG, Artemenko AG, Andronati SA. Interpretation of QSAR models based on random forest methods. Mol Inf 2011, 30:593-603.
-
(2011)
Mol Inf
, vol.30
, pp. 593-603
-
-
Kuz'min, V.E.1
Polishchuk, P.G.2
Artemenko, A.G.3
Andronati, S.A.4
-
44
-
-
33846887419
-
Contemporary QSAR classifiers compared
-
Bruce CL, Melville JL, Pickett SD, Hirst JD. Contemporary QSAR classifiers compared. J Chem Inf Model 2007, 47:219-227.
-
(2007)
J Chem Inf Model
, vol.47
, pp. 219-227
-
-
Bruce, C.L.1
Melville, J.L.2
Pickett, S.D.3
Hirst, J.D.4
-
45
-
-
42149179358
-
Hidden active information in a random compound library: extraction using a pseudo-structure-activity relationship model
-
Fukunishi H, Teramoto R, Shimada J. Hidden active information in a random compound library: extraction using a pseudo-structure-activity relationship model. J Chem Inf Model 2008, 48:575-582.
-
(2008)
J Chem Inf Model
, vol.48
, pp. 575-582
-
-
Fukunishi, H.1
Teramoto, R.2
Shimada, J.3
-
46
-
-
84862667032
-
An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential
-
McCarren P, Springer C, Whitehead L. An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential. J. Cheminformatics 2011, 3:51.
-
(2011)
J. Cheminformatics
, vol.3
, pp. 51
-
-
McCarren, P.1
Springer, C.2
Whitehead, L.3
-
47
-
-
77957730873
-
Predicting phospholipidosis using machine learning
-
Lowe R, Glen RC, Mitchell JBO. Predicting phospholipidosis using machine learning. Mol Pharm 2010, 7:1708-1718.
-
(2010)
Mol Pharm
, vol.7
, pp. 1708-1718
-
-
Lowe, R.1
Glen, R.C.2
Mitchell, J.B.O.3
-
48
-
-
79958775318
-
Development and comparison of hERG Blocker classifiers: assessment on different datasets yields markedly different results
-
Marchese Robinson RL, Glen RC, Mitchell JBO. Development and comparison of hERG Blocker classifiers: assessment on different datasets yields markedly different results. Mol Inf 2011, 30:443-458.
-
(2011)
Mol Inf
, vol.30
, pp. 443-458
-
-
Marchese Robinson, R.L.1
Glen, R.C.2
Mitchell, J.B.O.3
-
49
-
-
23844503717
-
Application of the random forest method in studies of local lymph node assay based skin sensitization data
-
Li S, Fedorowicz A, Singh H, Soderholm SC. Application of the random forest method in studies of local lymph node assay based skin sensitization data. J Chem Inf Model 2005, 45:952-964.
-
(2005)
J Chem Inf Model
, vol.45
, pp. 952-964
-
-
Li, S.1
Fedorowicz, A.2
Singh, H.3
Soderholm, S.C.4
-
50
-
-
37149053761
-
Targeted crystallisation of novel carbamazepine solvates based on a retrospective random forest classification
-
Johnston A, Johnston BF, Kennedy AR, Florence AJ. Targeted crystallisation of novel carbamazepine solvates based on a retrospective random forest classification. CrystEngComm 2008, 10:23-25.
-
(2008)
CrystEngComm
, vol.10
, pp. 23-25
-
-
Johnston, A.1
Johnston, B.F.2
Kennedy, A.R.3
Florence, A.J.4
-
51
-
-
33846856225
-
Random forest models to predict aqueous solubility
-
Palmer DS, O'Boyle NM, Glen RC, Mitchell JBO. Random forest models to predict aqueous solubility. J Chem Inf Model 2007, 47:150-158.
-
(2007)
J Chem Inf Model
, vol.47
, pp. 150-158
-
-
Palmer, D.S.1
O'Boyle, N.M.2
Glen, R.C.3
Mitchell, J.B.O.4
-
52
-
-
39449138204
-
Why are some properties more difficult to predict than others? A study of QSPR models of solubility, melting point, and log P
-
Hughes LD, Palmer DS, Nigsch F, Mitchell JBO. Why are some properties more difficult to predict than others? A study of QSPR models of solubility, melting point, and log P. J Chem Inf Model 2008, 48:220-232.
-
(2008)
J Chem Inf Model
, vol.48
, pp. 220-232
-
-
Hughes, L.D.1
Palmer, D.S.2
Nigsch, F.3
Mitchell, J.B.O.4
-
53
-
-
77952825581
-
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking
-
Ballester PJ, Mitchell JBO. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 2010, 26:1169-1175.
-
(2010)
Bioinformatics
, vol.26
, pp. 1169-1175
-
-
Ballester, P.J.1
Mitchell, J.B.O.2
-
54
-
-
27444445346
-
PostDOCK: a structural, empirical approach to scoring protein ligand complexes
-
Springer C, Adalsteinsson H, Young MM, Kegelmeyer PW, Roe DC. PostDOCK: a structural, empirical approach to scoring protein ligand complexes. J Med Chem 2005, 48:6821-6831.
-
(2005)
J Med Chem
, vol.48
, pp. 6821-6831
-
-
Springer, C.1
Adalsteinsson, H.2
Young, M.M.3
Kegelmeyer, P.W.4
Roe, D.C.5
-
55
-
-
75749126524
-
Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening
-
Sato T, Honma T, Yokoyama S. Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening. J Chem Inf Model 2010, 50:170-185.
-
(2010)
J Chem Inf Model
, vol.50
, pp. 170-185
-
-
Sato, T.1
Honma, T.2
Yokoyama, S.3
-
56
-
-
84883250593
-
RF: a random forest-based scoring function for improved affinity prediction of protein-ligand complexes
-
RF: a random forest-based scoring function for improved affinity prediction of protein-ligand complexes. J Chem Inf Model 2013, 53:1923-1933.
-
(2013)
J Chem Inf Model
, vol.53
, pp. 1923-1933
-
-
Zilian, D.1
Sotriffer, C.A.2
-
57
-
-
33845703344
-
What is a support vector machine?
-
Noble WS. What is a support vector machine? Nat Biotech 2006, 24:1565-1567.
-
(2006)
Nat Biotech
, vol.24
, pp. 1565-1567
-
-
Noble, W.S.1
-
58
-
-
69549111057
-
Cutting-plane training of structural SVMs
-
Joachims T, Finley T, Yu CN. Cutting-plane training of structural SVMs. Mach Learn 2009, 77:27-59.
-
(2009)
Mach Learn
, vol.77
, pp. 27-59
-
-
Joachims, T.1
Finley, T.2
Yu, C.N.3
-
59
-
-
0036505670
-
A comparison of methods for multiclass support vector machines
-
Hsu CW, Lin CJ. A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 2002, 13:415-425.
-
(2002)
IEEE Trans Neural Netw
, vol.13
, pp. 415-425
-
-
Hsu, C.W.1
Lin, C.J.2
-
60
-
-
84880072796
-
Drug repositioning: a machine-learning approach through data integration
-
Napolitano F, Zhao Y, Moreira V, Tagliaferri R, Kere J, D'Amato M, Greco D. Drug repositioning: a machine-learning approach through data integration. J Cheminf 2013, 5:30.
-
(2013)
J Cheminf
, vol.5
, pp. 30
-
-
Napolitano, F.1
Zhao, Y.2
Moreira, V.3
Tagliaferri, R.4
Kere, J.5
D'Amato, M.6
Greco, D.7
-
61
-
-
79952178127
-
A machine learning-based method to improve docking scoring functions and its application to drug repurposing
-
Kinnings SL, Liu N, Tonge PJ, Jackson RM, Xie L, Bourne PE. A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J Chem Inf Model 2011, 51:408-419.
-
(2011)
J Chem Inf Model
, vol.51
, pp. 408-419
-
-
Kinnings, S.L.1
Liu, N.2
Tonge, P.J.3
Jackson, R.M.4
Xie, L.5
Bourne, P.E.6
-
62
-
-
11844265914
-
Using support vector classification for SAR of fentanyl derivatives
-
Dong N, Lu WC, Chen NY, Zhu YC, Chen KX. Using support vector classification for SAR of fentanyl derivatives. Acta Pharmacol Sin 2005, 26:107-112.
-
(2005)
Acta Pharmacol Sin
, vol.26
, pp. 107-112
-
-
Dong, N.1
Lu, W.C.2
Chen, N.Y.3
Zhu, Y.C.4
Chen, K.X.5
-
63
-
-
39749088786
-
hERG classification model based on a combination of support vector machine method and GRIND descriptors
-
Li Q, Jørgensen FS, Oprea T, Brunak S, Taboureau O. hERG classification model based on a combination of support vector machine method and GRIND descriptors. Mol Pharm 2008, 5:117-127.
-
(2008)
Mol Pharm
, vol.5
, pp. 117-127
-
-
Li, Q.1
Jørgensen, F.S.2
Oprea, T.3
Brunak, S.4
Taboureau, O.5
-
64
-
-
77951675415
-
Prospective validation of a comprehensive in silico hERG model and its applications to commercial compound and drug databases
-
Doddareddy MR, Klaasse EC, Shagufta, IJzerman AP, Bender A. Prospective validation of a comprehensive in silico hERG model and its applications to commercial compound and drug databases. ChemMedChem 2010, 5:716-729.
-
(2010)
ChemMedChem
, vol.5
, pp. 716-729
-
-
Doddareddy, M.R.1
Klaasse, E.C.2
Shagufta3
IJzerman, A.P.4
Bender, A.5
-
65
-
-
36148963273
-
Prediction of mutagenic toxicity by combination of recursive partitioning and support vector machines
-
Liao Q, Yao J, Yuan S. Prediction of mutagenic toxicity by combination of recursive partitioning and support vector machines. Mol Divers 2007, 11:59-72.
-
(2007)
Mol Divers
, vol.11
, pp. 59-72
-
-
Liao, Q.1
Yao, J.2
Yuan, S.3
-
66
-
-
33646266941
-
Toxicity-indicating structural patterns
-
von Korff M, Sander T. Toxicity-indicating structural patterns. J Chem Inf Model 2006, 46:536-544.
-
(2006)
J Chem Inf Model
, vol.46
, pp. 536-544
-
-
von Korff, M.1
Sander, T.2
-
67
-
-
84864185732
-
Structure based model for the prediction of phospholipidosis induction potential of small molecules
-
Sun H, Shahane S, Xia M, Austin CP, Huang R. Structure based model for the prediction of phospholipidosis induction potential of small molecules. J Chem Inf Model 2012, 52:1798-1805.
-
(2012)
J Chem Inf Model
, vol.52
, pp. 1798-1805
-
-
Sun, H.1
Shahane, S.2
Xia, M.3
Austin, C.P.4
Huang, R.5
-
68
-
-
79551534528
-
Prediction of aqueous solubility of druglike organic compounds using partial least squares, back-propagation network and support vector machine
-
Cao DS, Xu QS, Liang YZ, Chen X, Li HD. Prediction of aqueous solubility of druglike organic compounds using partial least squares, back-propagation network and support vector machine. J Chemometrics 2010, 24:584-595.
-
(2010)
J Chemometrics
, vol.24
, pp. 584-595
-
-
Cao, D.S.1
Xu, Q.S.2
Liang, Y.Z.3
Chen, X.4
Li, H.D.5
-
69
-
-
33845748728
-
Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization
-
Nigsch F, Bender A, van Buuren B, Tissen J, Nigsch E, Mitchell JBO. Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization. J Chem Inf Model 2006, 46:2412-2422.
-
(2006)
J Chem Inf Model
, vol.46
, pp. 2412-2422
-
-
Nigsch, F.1
Bender, A.2
van Buuren, B.3
Tissen, J.4
Nigsch, E.5
Mitchell, J.B.O.6
-
70
-
-
84860154266
-
EnzML: multi-label prediction of enzyme classes using InterPro signatures
-
De Ferrari L, Aitken S, van Hemert J, Goryanin I. EnzML: multi-label prediction of enzyme classes using InterPro signatures. BMC Bioinf 2012, 13:61.
-
(2012)
BMC Bioinf
, vol.13
, pp. 61
-
-
De Ferrari, L.1
Aitken, S.2
van Hemert, J.3
Goryanin, I.4
-
71
-
-
20444407285
-
k nearest neighbors QSAR modeling as a variational problem: theory and applications
-
Itskowitz P, Tropsha A. k nearest neighbors QSAR modeling as a variational problem: theory and applications. J Chem Inf Model 2005, 45:777-785.
-
(2005)
J Chem Inf Model
, vol.45
, pp. 777-785
-
-
Itskowitz, P.1
Tropsha, A.2
-
72
-
-
22144451452
-
A study on the influence of molecular properties in the psychoactivity of cannabinoid compounds
-
Honório KM, da Silva AB. A study on the influence of molecular properties in the psychoactivity of cannabinoid compounds. J Mol Model 2005, 11:200-209.
-
(2005)
J Mol Model
, vol.11
, pp. 200-209
-
-
Honório, K.M.1
da Silva, A.B.2
-
73
-
-
33244481088
-
Three-dimensional QSAR using the k-nearest neighbor method and its interpretation
-
Ajmani S, Jadhav K, Kulkarni SA. Three-dimensional QSAR using the k-nearest neighbor method and its interpretation. J Chem Inf Model 2006, 46:24-31.
-
(2006)
J Chem Inf Model
, vol.46
, pp. 24-31
-
-
Ajmani, S.1
Jadhav, K.2
Kulkarni, S.A.3
-
74
-
-
0029088410
-
Predicting mutagenicity of chemicals using topological and quantum chemical parameters: a similarity based study
-
Basak SC, Grunwald GD. Predicting mutagenicity of chemicals using topological and quantum chemical parameters: a similarity based study. Chemosphere 1995, 31:2529-2546.
-
(1995)
Chemosphere
, vol.31
, pp. 2529-2546
-
-
Basak, S.C.1
Grunwald, G.D.2
-
75
-
-
33244488773
-
A fully computational model for predicting percutaneous drug absorption
-
Neumann D, Kohlbacher O, Merkwirth C, Lengauer T. A fully computational model for predicting percutaneous drug absorption. J Chem Inf Model 2006, 46:424-429.
-
(2006)
J Chem Inf Model
, vol.46
, pp. 424-429
-
-
Neumann, D.1
Kohlbacher, O.2
Merkwirth, C.3
Lengauer, T.4
-
76
-
-
1842740111
-
Combinatorial QSAR of ambergris fragrance compounds
-
Kovatcheva A, Golbraikh A, Oloff S, Xiao YD, Zheng W, Wolschann P, Buchbauer G, Tropsha A. Combinatorial QSAR of ambergris fragrance compounds. J Chem Inf Comput Sci 2004, 44:582-595.
-
(2004)
J Chem Inf Comput Sci
, vol.44
, pp. 582-595
-
-
Kovatcheva, A.1
Golbraikh, A.2
Oloff, S.3
Xiao, Y.D.4
Zheng, W.5
Wolschann, P.6
Buchbauer, G.7
Tropsha, A.8
-
77
-
-
0033553898
-
Structure-camphor odour relationships using the generation and selection of pertinent descriptors approach
-
Zakarya D, Chastrette M, Tollabi M, Fkih-Tetouani S. Structure-camphor odour relationships using the generation and selection of pertinent descriptors approach. Chemometrics Intell Lab Syst 1999, 48:35-46.
-
(1999)
Chemometrics Intell Lab Syst
, vol.48
, pp. 35-46
-
-
Zakarya, D.1
Chastrette, M.2
Tollabi, M.3
Fkih-Tetouani, S.4
-
78
-
-
3042788380
-
Tailored similarity spaces for the prediction of physicochemical properties
-
Gute BD, Basak SC, Mills D, Hawkins DM. Tailored similarity spaces for the prediction of physicochemical properties. Internet Electr J Mol Des 2002, 1:374-387.
-
(2002)
Internet Electr J Mol Des
, vol.1
, pp. 374-387
-
-
Gute, B.D.1
Basak, S.C.2
Mills, D.3
Hawkins, D.M.4
-
79
-
-
33646271333
-
Model selection based on structural similarity-method description and application to water solubility prediction
-
Kuhne R, Ebert RU, Schuurmann G. Model selection based on structural similarity-method description and application to water solubility prediction. J Chem Inf Model 2006, 46:636-641.
-
(2006)
J Chem Inf Model
, vol.46
, pp. 636-641
-
-
Kuhne, R.1
Ebert, R.U.2
Schuurmann, G.3
-
80
-
-
33750328049
-
Application of QSPR to mixtures
-
Ajmani S, Rogers SC, Barley MH, Livingstone DJ. Application of QSPR to mixtures. J Chem Inf Model 2006, 46:2043-2055.
-
(2006)
J Chem Inf Model
, vol.46
, pp. 2043-2055
-
-
Ajmani, S.1
Rogers, S.C.2
Barley, M.H.3
Livingstone, D.J.4
-
81
-
-
0016381211
-
Substructural analysis. A novel approach to the problem of drug design
-
Cramer RD, Redl G, Berkhoff CE. Substructural analysis. A novel approach to the problem of drug design. J Med Chem 1974, 17:533-535.
-
(1974)
J Med Chem
, vol.17
, pp. 533-535
-
-
Cramer, R.D.1
Redl, G.2
Berkhoff, C.E.3
-
82
-
-
84862651254
-
Predicting the mechanism of phospholipidosis
-
Lowe R, Mussa HY, Nigsch F, Glen RC, Mitchell JBO. Predicting the mechanism of phospholipidosis. J Cheminformatics 2012, 4:2.
-
(2012)
J Cheminformatics
, vol.4
, pp. 2
-
-
Lowe, R.1
Mussa, H.Y.2
Nigsch, F.3
Glen, R.C.4
Mitchell, J.B.O.5
-
83
-
-
84883239935
-
In silico target predictions: defining a benchmarking dataset and comparison of performance of the multiclass Naïve Bayes and Parzen-Rosenblatt Window
-
Koutsoukas A, Lowe R, KalantarMotamedi Y, Mussa HY, Klaffke W, Mitchell JBO, Glen R, Bender A. In silico target predictions: defining a benchmarking dataset and comparison of performance of the multiclass Naïve Bayes and Parzen-Rosenblatt Window. J Chem Inf Model 2013, 53:1957-1966.
-
(2013)
J Chem Inf Model
, vol.53
, pp. 1957-1966
-
-
Koutsoukas, A.1
Lowe, R.2
KalantarMotamedi, Y.3
Mussa, H.Y.4
Klaffke, W.5
Mitchell, J.B.O.6
Glen, R.7
Bender, A.8
-
84
-
-
34247386376
-
Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds
-
Cannon EO, Amini A, Bender A, Sternberg MJE, Muggleton SH, Glen RC, Mitchell JBO. Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J Comput Aided Mol Des 2007, 21:269-280.
-
(2007)
J Comput Aided Mol Des
, vol.21
, pp. 269-280
-
-
Cannon, E.O.1
Amini, A.2
Bender, A.3
Sternberg, M.J.E.4
Muggleton, S.H.5
Glen, R.C.6
Mitchell, J.B.O.7
-
85
-
-
58149116805
-
Ligand-target prediction using Winnow and naive Bayesian algorithms and the implications of overall performance statistics
-
Nigsch F, Bender A, Jenkins JL, Mitchell JBO. Ligand-target prediction using Winnow and naive Bayesian algorithms and the implications of overall performance statistics. J Chem Inf Model 2008, 48:2313-2325.
-
(2008)
J Chem Inf Model
, vol.48
, pp. 2313-2325
-
-
Nigsch, F.1
Bender, A.2
Jenkins, J.L.3
Mitchell, J.B.O.4
-
86
-
-
0034300831
-
Technical note: naive Bayes for regression
-
Frank E, Trigg L, Holmes G, Witten IH. Technical note: naive Bayes for regression. Mach Learn 2000, 41:5-25.
-
(2000)
Mach Learn
, vol.41
, pp. 5-25
-
-
Frank, E.1
Trigg, L.2
Holmes, G.3
Witten, I.H.4
-
87
-
-
67249134507
-
Efficient ant colony optimization algorithms for structure- and ligand-based drug design
-
Korb O. Efficient ant colony optimization algorithms for structure- and ligand-based drug design. Chem Cent J 2009, 3:O10.
-
(2009)
Chem Cent J
, vol.3
-
-
Korb, O.1
-
88
-
-
79960707576
-
Classifying molecules using a sparse probabilistic kernel binary classifier
-
Lowe R, Mussa HY, Mitchell JBO, Glen RC. Classifying molecules using a sparse probabilistic kernel binary classifier. J Chem Inf Model 2011, 51:1539-1544.
-
(2011)
J Chem Inf Model
, vol.51
, pp. 1539-1544
-
-
Lowe, R.1
Mussa, H.Y.2
Mitchell, J.B.O.3
Glen, R.C.4
-
89
-
-
84880054720
-
Predicting the protein targets for athletic performance-enhancing substances
-
Mavridis L, Mitchell JBO. Predicting the protein targets for athletic performance-enhancing substances. J Cheminformatics 2013, 5:31.
-
(2013)
J Cheminformatics
, vol.5
, pp. 31
-
-
Mavridis, L.1
Mitchell, J.B.O.2
-
90
-
-
67349172950
-
Interpretable features for the activity prediction of short antimicrobial peptides using fuzzy logic
-
Mikut R, Hilpert K. Interpretable features for the activity prediction of short antimicrobial peptides using fuzzy logic. Int J Pept Res Ther 2009, 15:129-137.
-
(2009)
Int J Pept Res Ther
, vol.15
, pp. 129-137
-
-
Mikut, R.1
Hilpert, K.2
-
91
-
-
33645023990
-
Rough set-based proteochemometrics modeling of G-protein-coupled receptor-ligand interactions
-
Strömbergsson H, Prusis P, Midelfart H, Lapinsh M, Wikberg JE, Komorowski J. Rough set-based proteochemometrics modeling of G-protein-coupled receptor-ligand interactions. Proteins 2006, 63:24-34.
-
(2006)
Proteins
, vol.63
, pp. 24-34
-
-
Strömbergsson, H.1
Prusis, P.2
Midelfart, H.3
Lapinsh, M.4
Wikberg, J.E.5
Komorowski, J.6
-
92
-
-
41549125090
-
How to winnow actives from inactives: introducing molecular orthogonal sparse bigrams (MOSBs) and multiclass Winnow
-
Nigsch F, Mitchell JBO. How to winnow actives from inactives: introducing molecular orthogonal sparse bigrams (MOSBs) and multiclass Winnow. J Chem Inf Model 2008, 48:306-318.
-
(2008)
J Chem Inf Model
, vol.48
, pp. 306-318
-
-
Nigsch, F.1
Mitchell, J.B.O.2
-
93
-
-
49549094711
-
Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases
-
Nigsch F, Mitchell JBO. Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases. Toxicol Appl Pharmacol 2008, 231:225-234.
-
(2008)
Toxicol Appl Pharmacol
, vol.231
, pp. 225-234
-
-
Nigsch, F.1
Mitchell, J.B.O.2
-
94
-
-
33746275630
-
In silico classification of hERG channel blockers: a knowledge-based strategy
-
Dubus E, Ijjaali I, Petitet F, Michel A. In silico classification of hERG channel blockers: a knowledge-based strategy. ChemMedChem 2006, 1:622-630.
-
(2006)
ChemMedChem
, vol.1
, pp. 622-630
-
-
Dubus, E.1
Ijjaali, I.2
Petitet, F.3
Michel, A.4
-
95
-
-
28444485615
-
Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods
-
Asikainen A, Kolehmainen M, Ruuskanen J, Tuppurainen K. Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods. Chemosphere 2006, 62:658-673.
-
(2006)
Chemosphere
, vol.62
, pp. 658-673
-
-
Asikainen, A.1
Kolehmainen, M.2
Ruuskanen, J.3
Tuppurainen, K.4
-
96
-
-
33750321101
-
Comparative QSAR- and fragments distribution analysis of drugs, druglikes, metabolic substances, and antimicrobial compounds
-
Karakoc E, Sahinalp SC, Cherkasov A. Comparative QSAR- and fragments distribution analysis of drugs, druglikes, metabolic substances, and antimicrobial compounds. J Chem Inf Model 2006, 46:2167-2182.
-
(2006)
J Chem Inf Model
, vol.46
, pp. 2167-2182
-
-
Karakoc, E.1
Sahinalp, S.C.2
Cherkasov, A.3
-
97
-
-
31944435982
-
Feature extraction and classification of Chilean wines
-
Beltran N, Duartemermoud M, Bustos M, Salah S, Loyola E, Penaneira A, Jalocha J. Feature extraction and classification of Chilean wines. J Food Eng 2006, 75:1-10.
-
(2006)
J Food Eng
, vol.75
, pp. 1-10
-
-
Beltran, N.1
Duartemermoud, M.2
Bustos, M.3
Salah, S.4
Loyola, E.5
Penaneira, A.6
Jalocha, J.7
-
98
-
-
33947219434
-
kScore: a novel machine learning approach that is not dependent on the data structure of the training set
-
Oloff S, Muegge I. kScore: a novel machine learning approach that is not dependent on the data structure of the training set. J Comput Aided Mol Des 2007, 21:87-95.
-
(2007)
J Comput Aided Mol Des
, vol.21
, pp. 87-95
-
-
Oloff, S.1
Muegge, I.2
-
99
-
-
0038724207
-
The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models
-
Tropsha A, Gramatica P, Gombar VK. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 2003, 2:69-77.
-
(2003)
QSAR Comb Sci
, vol.2
, pp. 69-77
-
-
Tropsha, A.1
Gramatica, P.2
Gombar, V.K.3
-
100
-
-
84862848391
-
Machine learning methods for property prediction in chemoinformatics: quo vadis?
-
Varnek A, Baskin I. Machine learning methods for property prediction in chemoinformatics: quo vadis? J Chem Inf Model 2012, 52:1413-1437.
-
(2012)
J Chem Inf Model
, vol.52
, pp. 1413-1437
-
-
Varnek, A.1
Baskin, I.2
-
101
-
-
3242726813
-
Monte Carlo cross-validation for selecting a model and estimating the prediction error in multivariate calibration
-
Xu QS, Liang YZ, Du YP. Monte Carlo cross-validation for selecting a model and estimating the prediction error in multivariate calibration. J Chemometrics 2004, 18:112-120.
-
(2004)
J Chemometrics
, vol.18
, pp. 112-120
-
-
Xu, Q.S.1
Liang, Y.Z.2
Du, Y.P.3
-
102
-
-
37349097759
-
Y-Randomization and its variants in QSPR/QSAR
-
Rücker C, G R, Meringer M. y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model 2007, 47:2345-2357.
-
(2007)
J Chem Inf Model
, vol.47
, pp. 2345-2357
-
-
Rücker, C.1
Rücker, G.2
Meringer, M.3
-
103
-
-
65649138430
-
A systematic analysis of performance measures for classification tasks
-
Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manage 2009, 45:427-437.
-
(2009)
Inf Process Manage
, vol.45
, pp. 427-437
-
-
Sokolova, M.1
Lapalme, G.2
-
104
-
-
0033931867
-
Assessing the accuracy of prediction algorithms for classification: an overview
-
Baldi P, Brunak S, Chauvin Y, Andersen CAF, Nielsen H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000, 16:412-424.
-
(2000)
Bioinformatics
, vol.16
, pp. 412-424
-
-
Baldi, P.1
Brunak, S.2
Chauvin, Y.3
Andersen, C.A.F.4
Nielsen, H.5
-
105
-
-
33745420796
-
Assessing different classification methods for virtual screening
-
Plewczynski D, Spieser SAH, Koch U. Assessing different classification methods for virtual screening. J Chem Inf Model 2006, 46:1098-1106.
-
(2006)
J Chem Inf Model
, vol.46
, pp. 1098-1106
-
-
Plewczynski, D.1
Spieser, S.A.H.2
Koch, U.3
-
107
-
-
61949223011
-
Findings of the challenge to predict aqueous solubility
-
Hopfinger AJ, Esposito EX, Llinàs A, Glen RC, Goodman JM. Findings of the challenge to predict aqueous solubility. J Chem Inf Model 2009, 49:1-5.
-
(2009)
J Chem Inf Model
, vol.49
, pp. 1-5
-
-
Hopfinger, A.J.1
Esposito, E.X.2
Llinàs, A.3
Glen, R.C.4
Goodman, J.M.5
-
108
-
-
1842525842
-
Use of physicochemical calculation of pKa and CLogP to predict phospholipidosis-inducing potential: A case study with structurally related piperazines
-
Ploemen JPHTM, Kelder J, Hafmans T, van de Sandt H, van Burgsteden JA, Saleminki PJ, van Esch E. Use of physicochemical calculation of pKa and CLogP to predict phospholipidosis-inducing potential: a case study with structurally related piperazines. Exp Toxicol Pathol 2004, 55:347-355.
-
(2004)
Exp Toxicol Pathol
, vol.55
, pp. 347-355
-
-
Ploemen, J.P.H.T.M.1
Kelder, J.2
Hafmans, T.3
van de Sandt, H.4
van Burgsteden, J.A.5
Saleminki, P.J.6
van Esch, E.7
-
109
-
-
84904989398
-
-
R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2006. ISBN 3-900051-07-0.
-
R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2006. ISBN 3-900051-07-0.
-
-
-
-
110
-
-
72949101619
-
Interpretation of nonlinear QSAR models applied to Ames mutagenicity data
-
Carlsson L, Helgee EA, Boyer S. Interpretation of nonlinear QSAR models applied to Ames mutagenicity data. J Chem Inf Model 2009, 49:2551-2558.
-
(2009)
J Chem Inf Model
, vol.49
, pp. 2551-2558
-
-
Carlsson, L.1
Helgee, E.A.2
Boyer, S.3
-
111
-
-
84879570665
-
Beyond the scope of Free-Wilson analysis: building interpretable QSAR models with machine learning algorithms
-
Chen H, Carlsson L, Eriksson M, Varkonyi P, Norinder U, Nilsson I. Beyond the scope of Free-Wilson analysis: building interpretable QSAR models with machine learning algorithms. J Chem Inf Model 2013, 53:1324-1336.
-
(2013)
J Chem Inf Model
, vol.53
, pp. 1324-1336
-
-
Chen, H.1
Carlsson, L.2
Eriksson, M.3
Varkonyi, P.4
Norinder, U.5
Nilsson, I.6
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