Virtual Screening and Design with Machine Intelligence Applied to Pim‐1 Kinase Inhibitors

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Abstract
Ligand‐based virtual screening of large compound collections, combined with fast bioactivity determination, facilitate the discovery of bioactive molecules with desired properties. Here, chemical similarity based machine learning and label‐free differential scanning fluorimetry were used to rapidly identify new ligands of the anticancer target Pim‐1 kinase. The three‐dimensional crystal structure complex of human Pim‐1 with ligand bound revealed an ATP‐competitive binding mode. Generative de novo design with a recurrent neural network additionally suggested innovative molecular scaffolds. Results corroborate the validity of the chemical similarity principle for rapid ligand prototyping, suggesting the complementarity of similarity‐based and generative computational approaches.

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