Generating Customized Compound Libraries for Drug Discovery with Machine Intelligence

07 November 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Drug discovery
Deep learning
Generative model
Chemical language model
De novo design
Natural products


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