Stereochemistry-aware string-based molecular generation.

ABSTRACT: This study investigates the impact of incorporating stereochemical information, a crucial aspect of computational drug discovery and materials design, in molecular generative modeling. We present a detailed comparison of stereochemistry-aware and conventionally stereochemistry-unaware string-based generative approaches, utilizing both genetic algorithms and reinforcement learning-based techniques. To evaluate these models, we introduce novel benchmarks specifically designed to assess the importance of stereochemistry-aware generative modeling. Our results demonstrate that stereochemistry-aware models generally perform on par with or surpass conventional algorithms across various stereochemistry-sensitive tasks. However, we also observe that in scenarios where stereochemistry plays a less c

SUBMITTER: Tom G 

PROVIDER: S-EPMC12582147 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature