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Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field

Maciej Wójcikowski, Piotr Zielenkiewicz, Pawel Siedlecki

Journal of Cheminformatics · 2015

Abstract

Background. There has been huge progress in the open cheminformatics field in both methods and software development. Unfortunately, there has been little effort to unite those methods and software into one package. We here describe the Open Drug Discovery Toolkit (ODDT), which aims to fulfill the need for comprehensive and open source drug discovery software.

Results. The Open Drug Discovery Toolkit was developed as a free and open source tool for both computer aided drug discovery (CADD) developers and researchers. ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines. ODDT is an out-of-the-box solution designed to be easily customizable and extensible. Therefore, users are strongly encouraged to extend it and develop new methods. We here present three use cases for ODDT in common tasks in computer-aided drug discovery.

Conclusion. Open Drug Discovery Toolkit is released on a permissive 3-clause BSD license for both academic and industrial use. ODDT’s source code, additional examples and documentation are available on GitHub (https://github.com/oddt/oddt).

Electronic supplementary material. The online version of this article (doi:10.1186/s13321-015-0078-2) contains supplementary material, which is available to authorized users.

References 26

1.
Vogt M, Bajorath J. Chemoinformatics: a view of the field and current trends in method development. Bioorg Med Chem. 2012;20:5317-5323.
Not in our library10.1016/j.bmc.2012.03.030
2.
Duffy BC, Zhu L, Decornez H, Kitchen DB. Early phase drug discovery: cheminformatics and computational techniques in identifying lead series. Bioorg Med Chem. 2012;20:5324-5342.
Not in our library10.1016/j.bmc.2012.04.062
3.
Open Babel: An open chemical toolbox · 2011
No notice
4.
RDKit: Cheminformatics and Machine Learning Software (2013). http://www.rdkit.org. Accessed 31 Nov 2014
Not in our library
5.
OpenEye Scientific Software, Santa Fe, NM, USA. http://www.eyesopen.com. Accessed 31 Nov 2014
Not in our library
6.
Jain AN. Scoring functions for protein-ligand docking. Curr Protein Pept Sci. 2006;7:407-420.
Not in our library10.2174/138920306778559395
7.
Cheng T, Li X, Li Y, Liu Z, Wang R. Comparative assessment of scoring functions on a diverse test set. J Chem Inf Model. 2009;49:1079-1093.
Not in our library10.1021/ci9000053
8.
Durrant JD, McCammon JA. NNScore 2.0: a neural-network receptor-ligand scoring function. J Chem Inf Model. 2011;51:2897-2903.
Not in our library10.1021/ci2003889
9.
Ballester PJ, Mitchell JBO. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinf Oxf Engl. 2010;26:1169-1175.
Not in our library10.1093/bioinformatics/btq112
10.
Zilian D, Sotriffer CA. SFCscore(RF): a random forest-based scoring function for improved affinity prediction of protein-ligand complexes. J Chem Inf Model. 2013;53:1923-1933.
Not in our library10.1021/ci400120b
11.
Cabrera ÁC, Gil-Redondo R, Perona A, Gago F, Morreale A. VSDMIP 1.5: an automated structure- and ligand-based virtual screening platform with a PyMOL graphical user interface. J Comput Aided Mol Des. 2011;25:813-824.
Not in our library10.1007/s10822-011-9465-6
12.
Wójcikowski M, Zielenkiewicz P, Siedlecki P. DiSCuS: an open platform for (not only) virtual screening results management. J Chem Inf Model. 2014;54:347-354.
Not in our library10.1021/ci400587f
13.
O’Boyle NM, Hutchison GR. Cinfony–combining Open Source cheminformatics toolkits behind a common interface. Chem Cent J. 2008;2:24.
Not in our library10.1186/1752-153x-2-24
14.
van der Walt S, Colbert SC, Varoquaux G. The NumPy Array: a structure for efficient numerical computation. Comput Sci Eng. 2011;13:22-30.
Not in our library10.1109/mcse.2011.37
15.
Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. 2004;1:337-341.
Not in our library10.1016/j.ddtec.2004.11.007
16.
Congreve M, Carr R, Murray C, Jhoti H. A “rule of three” for fragment-based lead discovery? Drug Discov Today. 2003;8:876-877.
Not in our library10.1016/s1359-6446(03)02831-9
17.
Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53:2719-2740.
Not in our library10.1021/jm901137j
18.
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009;30:2785-2791.
Not in our library10.1002/jcc.21256
19.
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455-461.
Not in our library10.1002/jcc.21334
20.
Jones G, Willett P, Glen RC. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol. 1995;245:43-53.
Not in our library10.1016/s0022-2836(95)80037-9
21.
Wojciechowski M (2007) FFNET: feed-forward neural network for Python. Tech Univ Lodz Pol Lodz Pol
Not in our library
22.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825-2830.
Not in our library
23.
Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc. 1988;110:5959-5967.
Not in our library10.1021/ja00226a005
24.
Schneider G, Downs G. Editorial: machine learning methods in QSAR modelling. QSAR Comb Sci. 2003;22:485-486.
Not in our library10.1002/qsar.200330825
25.
Niu B, Lu W, Yang S, Cai Y, Li G. Support vector machine for SAR/QSAR of phenethyl-amines. Acta Pharmacol Sin. 2007;28:1075-1086.
Not in our library10.1111/j.1745-7254.2007.00573.x
26.
Liu Z, Li Y, Han L, Li J, Zhao Z, Nie W, Liu Y, Wang R. PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics. 2015;31:405-412.
Not in our library10.1093/bioinformatics/btu626

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