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?:abstract
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Artificial intelligence and multi-objective optimization represent promising solutions to bridge chemical and biological landscape by addressing the automated de novo design of compounds as a result of a human-like creative process. In the present study, we conceived a novel pair based multi-objective approach implemented in an adapted SMILES generative algorithm based on Recurrent Neural Networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting Neuraminidase, Acetylcholinesterase and the main protease of Severe Acute Respiratory Syndrome Coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multi-objective optimization allow to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all the data are available as Supporting Information.
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