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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.<\/jats:p>","DOI":"10.1186\/s13321-021-00501-7","type":"journal-article","created":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T15:13:08Z","timestamp":1616080388000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES"],"prefix":"10.1186","volume":"13","author":[{"given":"Yongbeom","family":"Kwon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1174-4358","authenticated-orcid":false,"given":"Juyong","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,18]]},"reference":[{"issue":"25","key":"501_CR1","doi-asserted-by":"publisher","first-page":"10595","DOI":"10.1021\/jp960518i","volume":"100","author":"C Kuhn","year":"1996","unstructured":"Kuhn C, Beratan DN (1996) Inverse strategies for molecular design. 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