MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES

Journal of Cheminformatics

Juyong Lee¹  Yongbeom Kwon² 

[1] Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, 1 Gangwondaehak-gil, 24341, Chuncheon, Republic of Korea;Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, 1 Gangwondaehak-gil, 24341, Chuncheon, Republic of Korea;Arontier Inc., 15F, 241, Gangnam-daero, Seocho-gu, 06735, Seoul, Republic of Korea;

关键词: Molecular optimization;    SMILES;    Evolutionary algorithm;    Chemical space;   

DOI  :  10.1186/s13321-021-00501-7

来源: Springer

【 摘 要 】

Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods.

【 授权许可】

CC BY