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A specific phenomenon has recently been highlighted: goal-directed generation guided with machine learning models produce molecules with high scores according to the optimization model, but low scores according to control models, even when trained on the same data distribution and the same target. In this work, we show that this worrisome behavior is actually due to issues with the predictive models and not the goal-directed generation algorithms. We show that with appropriate predictive models, this issue can be resolved, and molecules generated have high scores according to both the optimization and the control models.<\/jats:p>","DOI":"10.1186\/s13321-022-00601-y","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T06:13:27Z","timestamp":1648793607000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Explaining and avoiding failure modes in goal-directed generation of small molecules"],"prefix":"10.1186","volume":"14","author":[{"given":"Maxime","family":"Langevin","sequence":"first","affiliation":[]},{"given":"Rodolphe","family":"Vuilleumier","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4345-995X","authenticated-orcid":false,"given":"Marc","family":"Bianciotto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,1]]},"reference":[{"issue":"3","key":"601_CR1","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1021\/acs.jcim.8b00839","volume":"59","author":"N Brown","year":"2019","unstructured":"Brown N, Fiscato M, Segler MHS, Vaucher AC (2019) GuacaMol: benchmarking models for de novo molecular design. 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