Is fragment-based graph a better graph-based molecular representation for drug design? A comparison study of graph-based models

• Published in: Computers in biology and medicine Vol. 169; p. 107811
• Main Authors: Chen, Baiyu, Pan, Ziqi, Mou, Minjie, Zhou, Yuan, Fu, Wei
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2024; Elsevier Limited
• Subjects: AI-Driven drug design; Algorithms; Artificial intelligence; Benchmarking; Broken symmetry; Datasets; Deep learning; Design; Drug Design; Drug development; Fragment-based representation; Fragmentation; Functional groups; Graph neural networks; Graph representations; Graphical representations; Machine learning; Models, Molecular; Neural networks; Neural Networks, Computer; Fragment-based representation; Graph neural networks; AI-Driven drug design
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107811

Graph Neural Networks (GNNs) have gained significant traction in various sectors of AI-driven drug design. Over recent years, the integration of fragmentation concepts into GNNs has emerged as a potent strategy to augment the efficacy of molecular generative models. Nonetheless, challenges such as symmetry breaking and potential misrepresentation of intricate cycles and undefined functional groups raise questions about the superiority of fragment-based graph representation over traditional methods. In our research, we undertook a rigorous evaluation, contrasting the predictive prowess of eight models—developed using deep learning algorithms—across 12 benchmark datasets that span a range of properties. These models encompass established methods like GCN, AttentiveFP, and D-MPNN, as well as innovative fragment-based representation techniques. Our results indicate that fragment-based methodologies, notably PharmHGT, significantly improve model performance and interpretability, particularly in scenarios characterized by limited data availability. However, in situations with extensive training, fragment-based molecular graph representations may not necessarily eclipse traditional methods. In summation, we posit that the integration of fragmentation, as an avant-garde technique in drug design, harbors considerable promise for the future of AI-enhanced drug design. •Pioneering Fragmentation Integration in GNNs.•Rigorous Model Comparison Across 12 Datasets.•Fragment-Based Methods Excel in Limited Data.•Assessing Traditional vs. Fragment-Based Approaches.•Research's Role in AI-Driven Drug Design.