1. bookVolume 8 (2018): Issue 3 (July 2018)
Journal Details
License
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

Soft Computing Tools for Virtual Drug Discovery

Published Online: 09 Feb 2018
Volume & Issue: Volume 8 (2018) - Issue 3 (July 2018)
Page range: 173 - 189
Received: 03 Sep 2017
Accepted: 30 Aug 2017
Journal Details
License
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
Abstract

In this paper, we describe how several soft computing tools can be used to assist in high throughput screening of potential drug candidates. Individual small molecules (ligands) are assessed for their potential to bind to specific proteins (receptors). Committees of multilayer networks are used to classify protein-ligand complexes as good binders or bad binders, based on selected chemical descriptors. The novel aspects of this paper include the use of statistical analyses on the weights of single layer networks to select the appropriate descriptors, the use of Monte Carlo cross-validation to provide confidence measures of network performance (and also to identify problems in the data), the addition of new chemical descriptors to improve network accuracy, and the use of Self Organizing Maps to analyze the performance of the trained network and identify anomalies. We demonstrate the procedures on a large practical data set, and use them to discover a promising characteristic of the data. We also perform virtual screenings with the trained networks on a number of benchmark sets and analyze the results.

Keywords

[1] J. D. Durrant and J. A. McCammon, NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein/Ligand Complexes, J. Chem. Inf. Model, 50, 2010, 1865-1871.10.1021/ci100244vSearch in Google Scholar

[2] Oleg Trott and Arthur J. Olson, AutoDock Vina: Improving the speed and accuracy of docking wih a new scoring function, efficient optimization, and multithreading, J. Computational Chemistry, 31, 2009, 455-461.10.1002/jcc.21334Search in Google Scholar

[3] R. Wang and X. Fang and Y. Lu and S. Wang, The PDBbind Database: Collection of Binding Affinities for Protein-Ligand Complexes with Known Three-Dimensional Structures, J. Med. Chem, 47, 2004, 2977-2980.10.1021/jm030580lOpen DOISearch in Google Scholar

[4] Stefano Forli, Raccoon—AutoDock VS: an automated tool for preparing AutoDock virtual screenings, http://autodock.scripps.edu/resources/raccoon, Accessed: 2016-01-10.Search in Google Scholar

[5] G. M. Morris and R. Huey and W. Lindstrom and M. F. Sanner and R. K. Belew and D. S. Goodsell and A. J. Olson, Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility, J. Computational Chemistry, 16, 2009, 2785-2791.10.1002/jcc.21256Search in Google Scholar

[6] P. G. Polishchuk and T. I. Madzhidov and A. Varnek, Estimation of the size of drug-like chemical space based on GDB-17 data, J. Computer Aided Molecule Design, 8, 2013, 675-679.10.1007/s10822-013-9672-4Search in Google Scholar

[7] Guo-Bo Li and Ling-Ling Yang and Wen-Jing Wang and Lin-Li Li and Sheng-Yong Yang, IDScore: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to ProteinLigand Interactions, J. Chem. Inf. Modeling, 53, 2013, 592-600.10.1021/ci300493wSearch in Google Scholar

[8] Daniel M. Hagan, and Martin T. Hagan, Virtual drug screening using neural networks, International Joint Conference on Neural Networks (IJCNN), pp. 579-587. IEEE, 2016.10.1109/IJCNN.2016.7727252Search in Google Scholar

[9] Martin Moller, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, 6, 1993, 525-533.10.1016/S0893-6080(05)80056-5Open DOISearch in Google Scholar

[10] M. T. Hagan and H. B. Demuth and M. H. Beale, Neural Network Design, PWS, 1996.Search in Google Scholar

[11] H. B. Demuth and M. H. Beale and M. T. Hagan, The Neural Network Toolbox for MATLAB, The MathWorks, 2014.Search in Google Scholar

[12] Kohonen, T., The self-organizing map, Proceedings of the IEEE, 78, 1990, 1464-1480.10.1109/5.58325Open DOISearch in Google Scholar

[13] T. J. Cheng, and X. Li, and Y. Li, and Z. H. Liu, and R. X. Wang, Comparative assessment of scoring functions on a diverse test set, J. Chem. Inf. Modeling, 49, 2009, 1079-1093.10.1021/ci900005319358517Search in Google Scholar

[14] Shoichet Huang and J. Irwin, Benchmarking Sets for Molecular Docking, Journal of Med. Chemistry, 49, 2006, 6789-7801.10.1021/jm0608356338331717154509Search in Google Scholar

[15] N. Triballeau, F. Archer, I. Brabet, J. P. Pin and H. O. Bertrand, Virtual screening workflow development guided by the receiver operating characteristic curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4, Journal of Med. Chemistry, 48, 2005, 2534-2547.10.1021/jm049092j15801843Search in Google Scholar

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