A molecular evolution algorithm for ligand design in DOCK.
J Comput Chem
; 43(29): 1942-1963, 2022 11 05.
Article
in English
| MEDLINE | ID: covidwho-2013553
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Drug Design
/
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
J Comput Chem
Journal subject:
Chemistry
Year:
2022
Document Type:
Article
Affiliation country:
Jcc.26993
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