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Beam Search Sampling for Molecular Design and Intrinsic Prioritization with Machine Intelligence

preprint
submitted on 03.03.2021, 16:20 and posted on 04.03.2021, 11:49 by Michael Moret, Moritz Helmstädter, Francesca Grisoni, Gisbert Schneider, Daniel Merk
Chemical language models enable de novo drug design without the requirement for explicit molecular construction rules. While such models have been applied to generate novel compounds with desired bioactivity, the actual prioritization and selection of the most promising computational designs remains challenging. In this work, we leveraged the probabilities learnt by chemical language models with the beam search algorithm as a model-intrinsic technique for automated molecule design and scoring. Prospective application of this method yielded three novel inverse agonists of retinoic acid receptor-related orphan receptors (RORs). Each design was synthesizable in three reaction steps and presented low-micromolar to nanomolar potency towards RORg. This model-intrinsic sampling technique eliminates the strict need for external compound scoring functions, thereby further extending the applicability of generative artificial intelligence to data-driven drug discovery.

Funding

Swiss National Science Foundation

Federal Department of Economic Affairs Education and Research

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Novartis Forschungsstiftung

History

Email Address of Submitting Author

merk@pharmchem.uni-frankfurt.de

Institution

Goethe University Frankfurt

Country

Germany

ORCID For Submitting Author

0000-0002-5359-8128

Declaration of Conflict of Interest

G.S. declares a potential financial conflict of interest as a founder of inSili.com GmbH, Zurich, and in his role as consultant to the pharmaceutical industry.

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