The expectations of <i>in silico</i> fragment-based drug design and future challenges

AI is transforming FBDD by automating compound design, predicting fragment-protein interactions, and expanding chemical diversity through deep learning, generative models, and reinforcement learning. These tools accelerate hit-to-lead processes, improve drug properties, and support multi-objective optimisation. AI enables fragment generation, pocket-specific design, and large-scale virtual screening, aiding the targeting of challenging proteins and modalities such as PROTACs and molecular glues. Larger fragment libraries enhance model training; although experimental validation remains key, AI reduces development time, improves accuracy, and broadens FBDD's scope.