STNGS: a deep scaffold learning-driven generation and screening framework for discovering potential novel psychoactive substances.

ABSTRACT: The supervision of novel psychoactive substances (NPSs) is a global problem, and the regulation of NPSs was heavily relied on identifying structural matches in established NPSs databases. However, violators could circumvent legal oversight by altering the side chain structure of recognized NPSs and the existing methods cannot overcome the inaccuracy and lag of supervision. In this study, we propose a scaffold and transformer-based NPS generation and Screening (STNGS) framework to systematically identify and evaluate potential NPSs. A scaffold-based generative model and a rank function with four parts are contained by our framework. Our generative model shows excellent performance in the design and optimization of general molecules and NPS-like molecules by chemical space analysis and prope

SUBMITTER: Liu D 

PROVIDER: S-EPMC11684896 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature