A molecular evolution algorithm for ligand design in DOCK.

Autor: Prentis LE; Department of Biochemistry & Cell Biology, Stony Brook University, Stony Brook, New York, USA., Singleton CD; Department of Molecular Pharmacology, Stony Brook University, Stony Brook, New York, USA., Bickel JD; Department of Chemistry, Stony Brook University, Stony Brook, New York, USA., Allen WJ; Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York, USA., Rizzo RC; Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York, USA.; Institute of Chemical Biology & Drug Discovery, Stony Brook University, Stony Brook, New York, USA.; Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, New York, USA.
Jazyk: angličtina
Zdroj: Journal of computational chemistry [J Comput Chem] 2022 Nov 05; Vol. 43 (29), pp. 1942-1963. Date of Electronic Publication: 2022 Sep 08.
DOI: 10.1002/jcc.26993
Abstrakt: As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single-molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein-ligand systems. Ensemble-based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS-CoV-2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run-time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.
(© 2022 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.)
Databáze: MEDLINE
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