DigFrag as a digital fragmentation method used for artificial intelligence-based drug design.

ABSTRACT: Fragment-Based Drug Design (FBDD) plays a pivotal role in the field of drug discovery and development. The construction of high-quality fragment libraries is a critical step in FBDD. Conventional fragmentation approaches often rely on rigid rules and chemical intuition, limiting their adaptability to diverse molecular structures. The rapid development of Artificial Intelligence (AI) technology offers a transformative opportunity to rethink traditional methods. Here, we present DigFrag, a digital fragmentation method that highlights important substructures by focusing locally within the molecular graph. In addition, we feed the fragments segmented by machine intelligence and human expertise into the deep generative model to compare the preference for data from different sources. Experimenta

SUBMITTER: Yang R 

PROVIDER: S-EPMC11555370 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature