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.

生成式机器学习模型可从化学空间采样分子，而无需明确的设计规则。为了能够使用有限的培训数据进行创新性分子实体的生成设计，提出了用于定制化合物库生成的深度学习框架，该框架旨在通过按需使用类药物分子实体来丰富和扩展与药理相关的化学空间。这种从头设计的方法结合了最佳实践，用于生成结合了生物活性合成化合物和天然产物特征的分子，这是发现药物的主要灵感来源。结果表明，数据驱动的机器智能获得了隐性的化学知识，并生成了具有定制特性和结构多样性的新型分子。