Using a genetic algorithm to find molecules with good docking scores

A graph-based genetic algorithm (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software package, starting from randomly chosen ZINC database, for four different targets: Bacillus subtilis chorismate mutase (CM), human β 2 -adrenergic G protein-coupled receptor ( AR), DDR1 kinase domain (DDR1), and -cyclodextrin (BCD). By combined use of functional group filters a score modifier based on heuristic synthetic accessibility (SA) our approach identifies between ca 500 6,000 structurally diverse better than known binders screening total 400,000 8,000 selected database. Screening 250,000 database significantly more binders, exception CM, where conventional only 60 compounds compared 511 GA+Filter+SA. In case AR DDR1, GA+Filter+SA finds lower −9.0 −10.0. The GA+Filters+SA methodology thus effective in generating large set synthetically accessible very good particular target. An early incarnation was potential COVID-19 main protease submitted stages COVID Moonshot project, crowd-sourced initiative accelerate development antiviral.