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The computational construction of small organic molecules (de novo design), directly in a protein binding site, is an effective means for generating novel ligands tailored to fit the pocket environment. In this work, we present two new methods, which aim to improve de novo design outcomes using (1) biasing algorithms to prioritize selection and/or acceptance of fragments and torsions during growth, and (2) parallel‐based clustering and pruning algorithms to remove duplicate molecules as candidate fragment are added. Large‐scale testing encompassing thousands of simulations were employed to interrogate the methods in terms of multiple metrics which include numbers of duplicate molecules generated, pairwise‐similarity, focused library reconstruction rates, fragment and torsion frequencies, fragment and torsion rank scores, interaction energy and drug‐likeness scores, and 3D pose comparisons. The biasing algorithms, particularly those that include fragment and torsion components simultaneously, led to molecules that more closely mimicked the distributions of fragments and torsions found in drug‐like libraries. The new parallel‐based clustering and pruning algorithms, compared with the existing serial approach, also led to larger ensembles comprised of topologically unique molecules with much greater efficiency by removing redundant growth paths. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]