Structure-Based Drug Design with a Deep Hierarchical Generative Model.

ABSTRACT: Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with improvements in virtual ligand screening methods, these expanded libraries are having a notable impact on early drug design efforts. Yet screening-based methods still face scalability limits, due to computational constraints and the sheer scale of drug-like space. Machine learning approaches are overcoming these limitations by learning the fundamental intra- and intermolecular relationships in drug-target systems from existing data. Here, we introduce DrugHIVE, a deep hierarchical variational autoencoder that outperforms state-of-the-art autoregressive

SUBMITTER: Weller JA 

PROVIDER: S-EPMC11350878 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature