
AUTODIFF, a novel diffusion-based fragment-wise autoregressive generation model, can generate realistic molecules with valid structures and conformations for structure-based drug design.


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autodiff-autoregressive-diffusion-modeling-for-realistic-structure-based-drug-design


AUTODIFF: Autoregressive Diffusion Modeling for Realistic Structure-based Drug Design


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TACOGFN, a Generative Flow Network conditioned on protein pocket structure, efficiently explores the chemical space to generate novel molecules with high binding affinity, drug-likeness, and synthesizability.


tacogfn-a-target-conditioned-generative-flow-network-for-efficient-structure-based-drug-design


TacoGFN: A Target-Conditioned Generative Flow Network for Efficient Structure-Based Drug Design
