Multi-objective drug design with a scaffold-aware variational autoencoder.

ABSTRACT: Designing molecules with multiple properties of interest is a fundamental challenge in drug development. To tackle this, we have developed ScafVAE, an innovative scaffold-aware variational autoencoder designed for the in silico graph-based generation of multi-objective drug candidates. By integrating our proposed bond scaffold-based generation with perplexity-inspired fragmentation, we expand the accessible chemical space of the conventional fragment-based approach while preserving its high chemical validity. ScafVAE was pre-trained on a large dataset of molecules and further augmented through contrastive learning and molecular fingerprint reconstruction, resulting in high accuracy in predicting various computationally and experimentally measured molecular properties. Only a few of

SUBMITTER: Dong T 

PROVIDER: S-EPMC12188581 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature