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In this work, we introduce AutoFragDiff, a fragment-based autoregressivediffusion model for generating 3D molecular structures conditioned on targetprotein structures. We employ geometric vector perceptrons to predict atomtypes and spatial coordinates of new molecular fragments conditioned onmolecular scaffolds and protein pockets. Our approach improves the localgeometry of the resulting 3D molecules while maintaining high predicted bindingaffinity to protein targets. The model can also perform scaffold extension fromuser-provided starting molecular scaffold.

Autoregressive fragment-based diffusion for pocket-aware ligand design