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

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. Experimental results show that the structural diversity of fragments segmented by DigFrag is higher, and more desirable compounds are generated based on these fragments. These results also demonstrate that data generated based on AI methods may be more suitable for AI models. Moreover, a user-friendly platform called MolFrag (https://dpai.ccnu.edu.cn/MolFrag/) is developed based on various fragmentation techniques to support molecular segmentation. Fragment-based drug design plays a pivotal role in the field of drug discovery and development, however, the construction of high-quality fragment libraries is a critical but challenging step. Here, the authors develop DigFrag, a digital fragmentation method based on the graph attention mechanism, showing higher structural diversity of the fragments and higher applicability to artificial intelligence-based drug design. [ABSTRACT FROM AUTHOR]