Generative molecular design in low data regimes

Abstract

Generative machine learning models sample molecules from chemical space without the need for explicit design rules. To enable the generative design of innovative molecular entities with limited training data, a deep learning framework for customized compound library generation is presented that aims to enrich and expand the pharmacologically relevant chemical space with drug-like molecular entities on demand. This de novo design approach combines best practices and was used to generate molecules that incorporate features of both 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.