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• W2896916724 endingPage "4703" @default.
• W2896916724 startingPage "4695" @default.
• W2896916724 abstract "At the early stages of the drug development process, thousands of compounds are synthesized in order to attain the best possible potency and pharmacokinetic properties. Once successful scaffolds are identified, large libraries of analogues are made, which is a challenging and time-consuming task. Recently, late stage functionalization (LSF) has become increasingly prominent since these reactions selectively functionalize C–H bonds, allowing to quickly produce analogues. Classical electrophilic aromatic halogenations are a powerful type of reaction in the LSF toolkit. However, the introduction of an electrophile in a regioselective manner on a drug-like molecule is a challenging task. Herein we present a machine learning model able to predict the reactive site of an electrophilic aromatic substitution with an accuracy of 93% (internal validation set). The model takes as input a SMILES of a compound and uses six quantum mechanics descriptors to identify its reactive site(s). On an external validation set, 90% of all molecules were correctly predicted." @default.
• W2896916724 created "2018-10-26" @default.
• W2896916724 creator A5007498431 @default.
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• W2896916724 date "2018-10-18" @default.
• W2896916724 modified "2023-10-06" @default.
• W2896916724 title "A Predictive Tool for Electrophilic Aromatic Substitutions Using Machine Learning" @default.
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• W2896916724 doi "https://doi.org/10.1021/acs.joc.8b02270" @default.
• W2896916724 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30336024" @default.
• W2896916724 hasPublicationYear "2018" @default.
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