Deep Generative Models for 3D Linker Design.

ABSTRACT: Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of three-dimensional (3D) structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural knowledge. Our method ("DeLinker") takes two fragments or partial structures and designs a molecule incorporating both. The generation process is protein-context-dependent, utilizing the relative distance and orientation between the partial structures. This 3D information is vital to successful compound design, and we demonstrate its impact on the generation process

SUBMITTER: Imrie F 

PROVIDER: S-EPMC7189367 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature