-
1
-
-
77649220192
-
Current trends in ligand-based virtual screening: Molecular representations, data mining methods, new application areas, and performance evaluation
-
Geppert H, Vogt M, Bajorath J: Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 2010, 50:205-216.
-
(2010)
J Chem Inf Model
, vol.50
, pp. 205-216
-
-
Geppert, H.1
Vogt, M.2
Bajorath, J.3
-
3
-
-
66849142177
-
How wrong can we get? A review of machine learning approaches and error bars
-
Schwaighofer A, Schroeter T, Mika S, Blanchard G: How wrong can we get? A review of machine learning approaches and error bars. Comb Chem High Throughput Screen 2009, 12:453-468.
-
(2009)
Comb Chem High Throughput Screen
, vol.12
, pp. 453-468
-
-
Schwaighofer, A.1
Schroeter, T.2
Mika, S.3
Blanchard, G.4
-
4
-
-
10244222365
-
Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures
-
Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A: Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. Org Biomol Chem 2004, 2:3256-3266.
-
(2004)
Org Biomol Chem
, vol.2
, pp. 3256-3266
-
-
Hert, J.1
Willett, P.2
Wilton, D.J.3
Acklin, P.4
Azzaoui, K.5
Jacoby, E.6
Schuffenhauer, A.7
-
5
-
-
70349941427
-
Virtual screening of Abl inhibitors from large compound libraries by support vector machines
-
Liu XH, Ma XH, Tan CY, Jiang YY, Go ML, Low BC, Chen YZ: Virtual screening of Abl inhibitors from large compound libraries by support vector machines. J Chem Inf Model 2009, 49:2101-2110.
-
(2009)
J Chem Inf Model
, vol.49
, pp. 2101-2110
-
-
Liu, X.H.1
Ma, X.H.2
Tan, C.Y.3
Jiang, Y.Y.4
Go, M.L.5
Low, B.C.6
Chen, Y.Z.7
-
6
-
-
33846887419
-
Contemporary QSAR classifiers compared
-
DOI 10.1021/ci600332j
-
Bruce CL, Melville JL, Pickett SD, Hirst JD: Contemporary QSAR classifiers compared. J Chem Inf Model 2007, 47:219-227. (Pubitemid 46225577)
-
(2007)
Journal of Chemical Information and Modeling
, vol.47
, Issue.1
, pp. 219-227
-
-
Brace, C.L.1
Melville, J.L.2
Pickett, S.D.3
Hirst, J.D.4
-
8
-
-
84877347172
-
Classification of cytochrome P 450 activities using machine learning methods
-
Hammann F, Gutmann H, Baumann U, Helma C, Drewe J: Classification of Cytochrome P 450 Activities Using Machine Learning Methods. Mol Pharmaceutics 2009, 33:796-801.
-
(2009)
Mol Pharmaceutics
, vol.33
, pp. 796-801
-
-
Hammann, F.1
Gutmann, H.2
Baumann, U.3
Helma, C.4
Drewe, J.5
-
9
-
-
33947245022
-
Evaluation of machine-learning methods for ligand-based virtual screening
-
DOI 10.1007/s10822-006-9096-5
-
Chen B, Harrison RF, Papadatos G, Willett P, Wood DJ, Lewell XQ, Greenidge P, Stiefl N: Evaluation of machine-learning methods for ligand-based virtual screening. J Comput Aided Mol Des 2007, 21:53-62. (Pubitemid 46416666)
-
(2007)
Journal of Computer-Aided Molecular Design
, vol.21
, Issue.1-3
, pp. 53-62
-
-
Chen, B.1
Harrison, R.F.2
Papadatos, G.3
Willett, P.4
Wood, D.J.5
Lewell, X.Q.6
Greenidge, P.7
Stiefl, N.8
-
10
-
-
43049157546
-
A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor
-
Han LY, Ma XH, Lin HH, Jia J, Zhu F, Xue Y, Li ZR, Cao ZW, Ji ZL, Chen YZ: A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. J Mol Graph Model 2008, 26:1276-1286.
-
(2008)
J Mol Graph Model
, vol.26
, pp. 1276-1286
-
-
Han, L.Y.1
Ma, X.H.2
Lin, H.H.3
Jia, J.4
Zhu, F.5
Xue, Y.6
Li, Z.R.7
Cao, Z.W.8
Ji, Z.L.9
Chen, Y.Z.10
-
11
-
-
33750982700
-
Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods
-
DOI 10.1016/j.jmgm.2006.01.007, PII S1093326306000180
-
Li H, Ung CY, Yap CW, Xue Y, Li ZR, Chen YZ: Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods. J Mol Graph Model 2006, 25:313-323. (Pubitemid 44740069)
-
(2006)
Journal of Molecular Graphics and Modelling
, vol.25
, Issue.3
, pp. 313-323
-
-
Li, H.1
Ung, C.Y.2
Yap, C.W.3
Xue, Y.4
Li, Z.R.5
Chen, Y.Z.6
-
12
-
-
13844312649
-
ZINC - A free database of commercially available compounds for virtual screening
-
DOI 10.1021/ci049714+
-
Irwin JJ, Shoichet BK ZINC: A Free Database of Commercially Available Compounds for Virtual Screening. J Chem Inf Model 2005, 45:177-182. (Pubitemid 40736970)
-
(2005)
Journal of Chemical Information and Modeling
, vol.45
, Issue.1
, pp. 177-182
-
-
Irwin, J.J.1
Shoichet, B.K.2
-
13
-
-
84877330182
-
-
MDDR Licensed By Accelrys Inc. USA
-
MDDR licensed by Accelrys, Inc. USA. www.accelrys.com.
-
-
-
-
15
-
-
41349093326
-
What do we know and when do we know it?
-
Nicholls A: What do we know and when do we know it? J Comput Aided Mol Des 2008, 22:239-255.
-
(2008)
J Comput Aided Mol des
, vol.22
, pp. 239-255
-
-
Nicholls, A.1
-
16
-
-
66249088724
-
Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries
-
Ma XH, Jia J, Zhu F, Xue Y, Li ZR, Chen YZ: Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries. Comb Chem High Throughput Screen 2009, 12:344-357.
-
(2009)
Comb Chem High Throughput Screen
, vol.12
, pp. 344-357
-
-
Ma, X.H.1
Jia, J.2
Zhu, F.3
Xue, Y.4
Li, Z.R.5
Chen, Y.Z.6
-
17
-
-
80255135618
-
Brainstorming: Weighted voting prediction of inhibitors for protein targets
-
Plewczynski D: Brainstorming: weighted voting prediction of inhibitors for protein targets. J Mol Model 2011, 17:2133-2141.
-
(2011)
J Mol Model
, vol.17
, pp. 2133-2141
-
-
Plewczynski, D.1
-
18
-
-
33947255489
-
Virtual high throughput screening using combined random forest and flexible docking
-
Plewczynski D, von Grotthuss M, Spieser SAH, Rychlewski L, Wyrwicz LS, Ginalski K, Koch U: Virtual high throughput screening using combined random forest and flexible docking. Comb Chem High Throughput Screen 2007, 10:189-196.
-
(2007)
Comb Chem High Throughput Screen
, vol.10
, pp. 189-196
-
-
Plewczynski, D.1
Von Grotthuss, M.2
Spieser, S.A.H.3
Rychlewski, L.4
Wyrwicz, L.S.5
Ginalski, K.6
Koch, U.7
-
19
-
-
70349879097
-
Turbo similarity searching: Effect of fingerprint and dataset on virtual-screening performance
-
Gardiner EJ, Gillet VJ, Haranczyk M, Hert J, Holliday JD, Malim N, Patel Y, Willet P: Turbo Similarity Searching: Effect of Fingerprint and Dataset on Virtual-Screening Performance. Stat Anal Data Min 2009, 2:103-114.
-
(2009)
Stat Anal Data Min
, vol.2
, pp. 103-114
-
-
Gardiner, E.J.1
Gillet, V.J.2
Haranczyk, M.3
Hert, J.4
Holliday, J.D.5
Malim, N.6
Patel, Y.7
Willet, P.8
-
22
-
-
84877323784
-
-
Discovery Studio provided by Accelrys, Inc USA
-
Discovery Studio, provided by Accelrys, Inc USA. www.accelrys.com.
-
-
-
-
23
-
-
84861400021
-
PubChem's BioAssay database
-
Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker BA, Bolton E, Gindulyte A, Bryant SH: PubChem's BioAssay Database. Nucleic Acids Res 2012, 40:D400-412.
-
(2012)
Nucleic Acids Res
, vol.40
-
-
Wang, Y.1
Xiao, J.2
Suzek, T.O.3
Zhang, J.4
Wang, J.5
Zhou, Z.6
Han, L.7
Karapetyan, K.8
Dracheva, S.9
Shoemaker, B.A.10
Bolton, E.11
Gindulyte, A.12
Bryant, S.H.13
-
24
-
-
79953005609
-
PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints
-
Yap CWEI: PaDEL-Descriptor: An Open Source Software to Calculate Molecular Descriptors and Fingerprints. J Comput Chem 2010, 32:1466-1474.
-
(2010)
J Comput Chem
, vol.32
, pp. 1466-1474
-
-
Yap, C.W.E.I.1
-
25
-
-
0037361967
-
The Chemistry Development Kit (CDK): An open-source Java library for Chemo- and Bioinformatics
-
Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E: The Chemistry Development Kit (CDK): an open-source Java library for Chemo- and Bioinformatics. J Chem Inf Comput Sci 2003, 43:493-500.
-
(2003)
J Chem Inf Comput Sci
, vol.43
, pp. 493-500
-
-
Steinbeck, C.1
Han, Y.2
Kuhn, S.3
Horlacher, O.4
Luttmann, E.5
Willighagen, E.6
-
27
-
-
54949134905
-
Chemical substructures that enrich for biological activity
-
Klekota J, Roth FP: Chemical substructures that enrich for biological activity. Bioinformatics 2008, 24:2518-2525.
-
(2008)
Bioinformatics
, vol.24
, pp. 2518-2525
-
-
Klekota, J.1
Roth, F.P.2
-
28
-
-
0003120218
-
Sequential minimal optimization: A fast algorithm for training support vector machines
-
Edited by Scholkopf B, Burges C, Smola AJ. Cambridge: MIT Press
-
Platt JC: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. In Advances in Kernel Methods - Support Vector Learning. Edited by Scholkopf B, Burges C, Smola AJ. Cambridge: MIT Press; 1999:185-208.
-
(1999)
Advances in Kernel Methods - Support Vector Learning
, pp. 185-208
-
-
Platt, J.C.1
-
30
-
-
84877326977
-
Comparing performance of committee based approaches to active learning
-
Edited by Klopotek M, Przepiorkowski A, Wierzchon S, Trojanowski K. Warsaw: EXIT
-
Stefanowski J, Pachocki M: Comparing Performance of Committee Based Approaches to Active Learning. In Recent Advances in Intelligent Information Systems. Edited by Klopotek M, Przepiorkowski A, Wierzchon S, Trojanowski K. Warsaw: EXIT; 2009:457-470.
-
(2009)
Recent Advances in Intelligent Information Systems
, pp. 457-470
-
-
Stefanowski, J.1
Pachocki, M.2
-
32
-
-
0035478854
-
Random forests
-
DOI 10.1023/A:1010933404324
-
Breiman L: Random Forests. Mach Learn 2001, 45:5-32. (Pubitemid 32933532)
-
(2001)
Machine Learning
, vol.45
, Issue.1
, pp. 5-32
-
-
Breiman, L.1
-
33
-
-
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
-
34
-
-
76749092270
-
The WEKA data mining software: An update
-
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH: The WEKA data mining software: an update. SIGKDD Explorations 2009, 11:10-18.
-
(2009)
SIGKDD Explorations
, vol.11
, pp. 10-18
-
-
Hall, M.1
Frank, E.2
Holmes, G.3
Pfahringer, B.4
Reutemann, P.5
Witten, I.H.6
-
35
-
-
80051681914
-
Prediction of the bonding state of cysteine residues in proteins with machine-learning methods
-
Edited by Rizzo R, Lisboa PJG. Berlin Heidelberg: Springer-Verlag
-
Savojardo C, Fariselli P, Martelli PL, Shukla P, Casadio R: Prediction of the Bonding State of Cysteine Residues in Proteins with Machine-Learning Methods. In Computational Intelligence Methods for Bioinformatics and Biostatistics 7th International Meeting. 6665th edition. Edited by Rizzo R, Lisboa PJG. Berlin Heidelberg: Springer-Verlag; 2011:98-111.
-
(2011)
Computational Intelligence Methods for Bioinformatics and Biostatistics 7th International Meeting. 6665th Edition
, pp. 98-111
-
-
Savojardo, C.1
Fariselli, P.2
Martelli, P.L.3
Shukla, P.4
Casadio, R.5
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