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Generating Customized Compound Libraries for Drug Discovery with Machine Intelligence

preprint
submitted on 01.11.2019 and posted on 07.11.2019 by Michael Moret, Lukas Friedrich, Francesca Grisoni, Daniel Merk, Gisbert Schneider

Generative machine learning models sample drug-like molecules from chemical space without the need for explicit design rules. A deep learning framework for customized compound library generation is presented, aiming to enrich and expand the pharmacologically relevant chemical space with new molecular entities ‘on demand’. This de novo design approach was used to generate molecules that combine features from bioactive synthetic compounds and natural products, which are a primary source of inspiration for drug discovery. The results show that the data-driven machine intelligence acquires implicit chemical knowledge and generates novel molecules with bespoke properties and structural diversity. The method is available as an open-access tool for medicinal and bioorganic chemistry.

Funding

Novartis Forschungsstiftung (FreeNovation: AI in Drug Discovery)

History

Email Address of Submitting Author

michael.moret@pharma.ethz.ch

Institution

ETHZ

Country

Switzerland

ORCID For Submitting Author

0000-0002-8672-3386

Declaration of Conflict of Interest

G.S. declares a potential financial conflict of interest as a consultant to the pharmaceutical industry and co-founder of inSili.com GmbH, Zurich. No other potential conflicts of interest are declared.

Version Notes

Work in progress.

Licence

Exports