Soft computing tools for virtual drug discovery

• Autorzy: Hagan D. , Hagan M.

Identyfikatory

DOI

10.1515/jaiscr-2018-0012

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

drug discovery; virtual screening; multilayer network; SOM

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2018
• Tom: Vol. 8, No. 3
• Strony: 173--189
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Hagan D.

• Department of Biochemistry, Oklahoma State University, Stillwater, OK, USA

autor

Hagan M.

• School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA

Bibliografia

• [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.
• [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.
• [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.
• [4] Stefano Forli, Raccoon—AutoDock VS: an automated tool for preparing AutoDock virtual screenings, http://autodock.scripps.edu/resources/raccoon,Accessed: 2016-01-10.
• [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.
• [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.
• [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.
• [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.
• [9] Martin Moller, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, 6, 1993, 525-533.
• [10] M. T. Hagan and H. B. Demuth and M. H. Beale, Neural Network Design, PWS, 1996.
• [11] H. B. Demuth and M. H. Beale and M. T. Hagan, The Neural Network Toolbox for MATLAB, The MathWorks, 2014.
• [12] Kohonen, T., The self-organizing map, Proceedings of the IEEE, 78, 1990, 1464-1480.
• [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.
• [14] Shoichet Huang and J. Irwin, Benchmarking Sets for Molecular Docking, Journal of Med. Chemistry, 49, 2006, 6789-7801.
• [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.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).