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Improvement of this molecular description is highly desirable for in silico drug discovery and medicinal chemistry applications. In this work, we revised a well\u2010established molecular mechanics' force field and applied a hybrid quantum mechanics and machine learning approach to parametrize the hydrogen\u2010bonding (HB) potentials of small molecules, improving this aspect of the molecular description. Approximately 66,000 molecules were chosen from available drug databases and subjected to density functional theory calculations (DFT). For each atom, the molecular electrostatic potential (EP) was extracted and used to derive new HB energy contributions; this was subsequently combined with a fingerprint\u2010based description of the structural environment via partial least squares modeling, enabling the new potentials to be used for molecules outside of the training set. We demonstrate that parameter prediction for molecules outside of the training set correlates with their DFT\u2010derived EP, and that there is correlation of the new potentials with hydrogen\u2010bond acidity and basicity scales. We show the newly derived MIFs vary in strength for various ring substitution in accordance with chemical intuition. Finally, we report that this derived parameter, when extended to non\u2010HB atoms, can also be used to estimate sites of reaction.<\/jats:p>","DOI":"10.1002\/jcc.26737","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T11:49:30Z","timestamp":1629373770000},"page":"2068-2078","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Combining machine learning and quantum mechanics yields more chemically aware<\/scp> molecular descriptors for medicinal chemistry applications"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-9691-8323","authenticated-orcid":false,"given":"Sara","family":"Tortorella","sequence":"first","affiliation":[{"name":"Molecular Horizon srl via Montelino 30 Bettona (Perugia) 06084 Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0657-5035","authenticated-orcid":false,"given":"Emanuele","family":"Carosati","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Biology and Biotechnology University of Perugia Perugia Italy"}]},{"given":"Giulia","family":"Sorbi","sequence":"additional","affiliation":[{"name":"Molecular Horizon srl via Montelino 30 Bettona (Perugia) 06084 Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9361-8437","authenticated-orcid":false,"given":"Giovanni","family":"Bocci","sequence":"additional","affiliation":[{"name":"Translational Informatics Division, Department of Internal Medicine University of New Mexico School of Medicine Albuquerque New Mexico USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8736-4397","authenticated-orcid":false,"given":"Simon","family":"Cross","sequence":"additional","affiliation":[{"name":"Molecular Discovery Ltd Hertfordshire UK"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4162-8692","authenticated-orcid":false,"given":"Gabriele","family":"Cruciani","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Biology and Biotechnology University of Perugia Perugia Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5021-7759","authenticated-orcid":false,"given":"Loriano","family":"Storchi","sequence":"additional","affiliation":[{"name":"Dipartimento di Farmacia Universit\u00e0 G. 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