-
1
-
-
0036835460
-
Integration of virtual and high throughput screening
-
Bajorath, J. Integration of virtual and high throughput screening. Nat. Rev. Drug Discov. 2002, 1, 882-894.
-
(2002)
Nat. Rev. Drug Discov.
, vol.1
, pp. 882-894
-
-
Bajorath, J.1
-
2
-
-
0036214750
-
Virtual screening: Methods, expectations, and reality
-
Bajorath, J. Virtual screening: methods, expectations, and reality. Curr. Drug Discovery 2002, 2, 24-28.
-
(2002)
Curr. Drug Discovery
, vol.2
, pp. 24-28
-
-
Bajorath, J.1
-
3
-
-
0036740917
-
Why do we need so many chemical similarity search methods?
-
Sheridan, R. P.; Kearsley, S. K. Why do we need so many chemical similarity search methods? Drug Discovery Today 2002, 7, 903-911.
-
(2002)
Drug Discovery Today
, vol.7
, pp. 903-911
-
-
Sheridan, R.P.1
Kearsley, S.K.2
-
4
-
-
0035438391
-
Is there a difference between leads and drugs? a historical perspective
-
Oprea, T. I.; Davis, A. M.; Teague, S. J.; Leeson, P. D. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 2001, 41, 1308-1315.
-
(2001)
J. Chem. Inf. Comput. Sci.
, vol.41
, pp. 1308-1315
-
-
Oprea, T.I.1
Davis, A.M.2
Teague, S.J.3
Leeson, P.D.4
-
5
-
-
0036589285
-
Current trends in lead discovery: Are we looking for the appropriate properties?
-
Oprea, T. I. Current trends in lead discovery: are we looking for the appropriate properties? J. Comput.-Aided Mol. Des. 2002, 16, 325-334.
-
(2002)
J. Comput.-aided Mol. Des.
, vol.16
, pp. 325-334
-
-
Oprea, T.I.1
-
7
-
-
27144489164
-
A tutorial on support vector machines for pattern recognition
-
Burges, C. J. C. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining Knowl. Discovery 1998, 2, 121-167.
-
(1998)
Data Mining Knowl. Discovery
, vol.2
, pp. 121-167
-
-
Burges, C.J.C.1
-
8
-
-
0034740222
-
Drug design by machine learning: Support vector machines for pharmaceutical data analysis
-
Burbidge, R.; Trotter, M.; Buxton, B.; Holden, S. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 2001, 26, 5-14.
-
(2001)
Comput. Chem.
, vol.26
, pp. 5-14
-
-
Burbidge, R.1
Trotter, M.2
Buxton, B.3
Holden, S.4
-
9
-
-
0037365194
-
Active learning with support vector machines in the drug discovery process
-
Warmuth, M. K.; Liao, J.; Ratsch, G.; Mathieson, M.; Putta, S.; Lemmen, C. Active learning with support vector machines in the drug discovery process. J. Chem. Inf. Comput. Sci. 2003, 43, 667-673.
-
(2003)
J. Chem. Inf. Comput. Sci.
, vol.43
, pp. 667-673
-
-
Warmuth, M.K.1
Liao, J.2
Ratsch, G.3
Mathieson, M.4
Putta, S.5
Lemmen, C.6
-
10
-
-
0345117343
-
Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP-glucuronosyltransferase isoforms
-
Sorich, M. J.; Miners, J. O.; McKinnon, R. A.; Winkler, D. A.; Burden, F. R.; Smith, P. A. Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP- glucuronosyltransferase isoforms. J. Chem. Inf. Comput. Sci. 2003, 43, 2019-2024.
-
(2003)
J. Chem. Inf. Comput. Sci.
, vol.43
, pp. 2019-2024
-
-
Sorich, M.J.1
Miners, J.O.2
McKinnon, R.A.3
Winkler, D.A.4
Burden, F.R.5
Smith, P.A.6
-
11
-
-
0344254815
-
Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions
-
Zernov, V. V.; Balakin, K. V.; Ivaschenko, A. A.; Savchuk, N. P.; Pletnev, I. V. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J. Chem. Inf. Comput. Sci. 2003, 43, 2048-2056.
-
(2003)
J. Chem. Inf. Comput. Sci.
, vol.43
, pp. 2048-2056
-
-
Zernov, V.V.1
Balakin, K.V.2
Ivaschenko, A.A.3
Savchuk, N.P.4
Pletnev, I.V.5
-
13
-
-
0003773721
-
Fast training of support vector machines using sequential minimal optimization
-
Platt, J. Fast Training of Support Vector Machines using Sequential Minimal Optimization; Microsoft Research Technical Report MSR-TR-98-14; 1998.
-
(1998)
Microsoft Research Technical Report
, vol.MSR-TR-98-14
-
-
Platt, J.1
-
14
-
-
0000545946
-
Improvements to Plait's SMO algorithm for SVM classifier design
-
Keerthi, S. S.; Shevade, S. K.; Bhattacharyya, C.; Murthy, K. R. K. Improvements to Plait's SMO Algorithm for SVM Classifier Design. Neural Comput. 2001, 13, 637-649.
-
(2001)
Neural Comput.
, vol.13
, pp. 637-649
-
-
Keerthi, S.S.1
Shevade, S.K.2
Bhattacharyya, C.3
Murthy, K.R.K.4
-
15
-
-
34249753618
-
Support vector networks
-
Cortes, C.; Vapnik, V. Support vector networks. Machine Learning 1995, 20, 273-297.
-
(1995)
Machine Learning
, vol.20
, pp. 273-297
-
-
Cortes, C.1
Vapnik, V.2
-
16
-
-
0035438388
-
Prediction of biological activity for high-throughput screening using binary kernel discrimination
-
Harper, O.; Bradshaw, J.; Gittins, J. C.; Green, D. V.; Leach, A. R. Prediction of biological activity for high-throughput screening using binary kernel discrimination. J. Chem. Inf. Comput. Sci. 2001, 41, 1295-1300.
-
(2001)
J. Chem. Inf. Comput. Sci.
, vol.41
, pp. 1295-1300
-
-
Harper, O.1
Bradshaw, J.2
Gittins, J.C.3
Green, D.V.4
Leach, A.R.5
-
17
-
-
0037363598
-
Comparison of ranking methods for virtual screening in lead-discovery programs
-
Wilton, D.; Willett, P.; Lawson, K.; Mullier, G. Comparison of ranking methods for virtual screening in lead-discovery programs. J. Chem. Inf. Comput. Sci. 2003, 43, 469-474.
-
(2003)
J. Chem. Inf. Comput. Sci.
, vol.43
, pp. 469-474
-
-
Wilton, D.1
Willett, P.2
Lawson, K.3
Mullier, G.4
-
18
-
-
3843072084
-
Feature selection in MLPs and SVMs based on maximum output information
-
Sindhwani, V.; Rakshit, S.; Deodhar, D.; Erdogmus, D.; Principe, J. C.; Niyogi, P. Feature Selection in MLPs and SVMs based on Maximum Output Information. IEEE Trans. Neural Netw. 2004, 15, 937-948.
-
(2004)
IEEE Trans. Neural Netw.
, vol.15
, pp. 937-948
-
-
Sindhwani, V.1
Rakshit, S.2
Deodhar, D.3
Erdogmus, D.4
Principe, J.C.5
Niyogi, P.6
-
19
-
-
0036161259
-
Gene selection for cancer classification using support vector machines
-
Guyon, I.; Weston, W.; Barnhill, S.; Vapnik, V. Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning 2002, 46, 389-422.
-
(2002)
Machine Learning
, vol.46
, pp. 389-422
-
-
Guyon, I.1
Weston, W.2
Barnhill, S.3
Vapnik, V.4
-
20
-
-
0033569406
-
Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
-
Golub, T. R.; Slonim, D. K.; Tamayo, P.; Huard, C.; Gaasenbeek, M.; Mesirov, J. P.; Coller, H.; Loh, M. L.; Downing, J. R.; Caligiuri, M. A.; Bloomfield, C. D.; Lander, E. S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286, 531-537.
-
(1999)
Science
, vol.286
, pp. 531-537
-
-
Golub, T.R.1
Slonim, D.K.2
Tamayo, P.3
Huard, C.4
Gaasenbeek, M.5
Mesirov, J.P.6
Coller, H.7
Loh, M.L.8
Downing, J.R.9
Caligiuri, M.A.10
Bloomfield, C.D.11
Lander, E.S.12
-
21
-
-
20444370028
-
-
MDL Information Systems, Inc.
-
IsisDraw 2.4, MDL Information Systems, Inc., 2001.
-
(2001)
IsisDraw 2.4
-
-
-
22
-
-
0001708959
-
Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited
-
Dalby, A.; Nourse, J. G.; Hounshell, W. D.; Gushurst, A. K. I.; Grier, D. L.; Leland, B. A.; Laufer, J. Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited. J. Chem. Inf. Comput. Sci. 1992, 32, 244-255.
-
(1992)
J. Chem. Inf. Comput. Sci.
, vol.32
, pp. 244-255
-
-
Dalby, A.1
Nourse, J.G.2
Hounshell, W.D.3
Gushurst, A.K.I.4
Grier, D.L.5
Leland, B.A.6
Laufer, J.7
-
23
-
-
84860947089
-
-
http://www.mdl.com/solutions/white_papers/ctfile_formats.jsp.
-
-
-
-
24
-
-
84860943820
-
-
http://dtp.nci.nih.gov/docs/3d_database/Structural_information/ structural_data.html.
-
-
-
-
25
-
-
84860943819
-
-
http://www.maybridge.com.
-
-
-
-
26
-
-
0000490166
-
From atoms and bonds to three-dimensional atomic coordinates: Automatic model builders
-
Sadowski, J.; Gasteiger, J. From Atoms and Bonds to Three-Dimensional Atomic Coordinates: Automatic Model Builders. Chem. Rev. 1993, 93, 2567-2581.
-
(1993)
Chem. Rev.
, vol.93
, pp. 2567-2581
-
-
Sadowski, J.1
Gasteiger, J.2
-
28
-
-
33749255357
-
-
Milano Chemometrics and QSAR Research Group: Milan, Italy
-
Todeschini, R.; Consonni, V.; Pavan, M. DRAGON 2.1. Milano Chemometrics and QSAR Research Group: Milan, Italy, 2002.
-
(2002)
DRAGON 2.1.
-
-
Todeschini, R.1
Consonni, V.2
Pavan, M.3
-
29
-
-
0001611357
-
JChem: Java applets and modules supporting chemical database handling from web browsers
-
Csizmadia F. JChem: Java applets and modules supporting chemical database handling from web browsers. J. Chem. Inf. Comput. Sci. 2000, 40, 323-324.
-
(2000)
J. Chem. Inf. Comput. Sci.
, vol.40
, pp. 323-324
-
-
Csizmadia, F.1
-
30
-
-
5344244908
-
Chemical similarity searching
-
Willett, P.; Barnard, J. M.; Downs, G. M. Chemical Similarity Searching. J. Chem. Inf. Comput. Sci. 1998, 38, 983-996.
-
(1998)
J. Chem. Inf. Comput. Sci.
, vol.38
, pp. 983-996
-
-
Willett, P.1
Barnard, J.M.2
Downs, G.M.3
-
31
-
-
0035865781
-
Improved scoring of ligand-protein interactions using OWFEG free energy grids
-
Pearlman, D. A.; Charifson, P. S. Improved scoring of ligand-protein interactions using OWFEG free energy grids. J. Med. Chem. 2001, 44, 502-511.
-
(2001)
J. Med. Chem.
, vol.44
, pp. 502-511
-
-
Pearlman, D.A.1
Charifson, P.S.2
-
32
-
-
1642310340
-
Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening
-
Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 2004, 47, 1750-1759.
-
(2004)
J. Med. Chem.
, vol.47
, pp. 1750-1759
-
-
Halgren, T.A.1
Murphy, R.B.2
Friesner, R.A.3
Beard, H.S.4
Frye, L.L.5
Pollard, W.T.6
Banks, J.L.7
-
33
-
-
0003102354
-
Similarity searching in files of three-dimensional chemical structures: Evaluation of the EVA descriptor and combination of rankings using data fusion
-
Ginn, C. M. R.; Turner, D. B.; Willett, P.; Ferguson, A. M.; Heritage, T. W. Similarity Searching in Files of Three-Dimensional Chemical Structures: Evaluation of the EVA Descriptor and Combination of Rankings Using Data Fusion. J. Chem. Inf. Comput. Sci. 1997, 37, 23-37.
-
(1997)
J. Chem. Inf. Comput. Sci.
, vol.37
, pp. 23-37
-
-
Ginn, C.M.R.1
Turner, D.B.2
Willett, P.3
Ferguson, A.M.4
Heritage, T.W.5
-
34
-
-
0032474873
-
Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices
-
Kubinyi, H.; Hamprecht, F. A.; Mietzner, T. Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. J. Med. Chem. 1998, 41, 2553-2564.
-
(1998)
J. Med. Chem.
, vol.41
, pp. 2553-2564
-
-
Kubinyi, H.1
Hamprecht, F.A.2
Mietzner, T.3
-
36
-
-
0042355453
-
Rational selection of training and test sets for the development of validated QSAR models
-
Golbraikh, A.; Shen, M.; Xiao, Z.; Xiao, Y. D.; Lee, K. H.; Tropsha, A. Rational selection of training and test sets for the development of validated QSAR models. J. Comput.-Aided Mol. Des. 2003, 17, 241-253.
-
(2003)
J. Comput.-aided Mol. Des.
, vol.17
, pp. 241-253
-
-
Golbraikh, A.1
Shen, M.2
Xiao, Z.3
Xiao, Y.D.4
Lee, K.H.5
Tropsha, A.6
|