Structure-based drug design (SBDD), which aims to generate molecules that can
bind tightly to the target protein, is an essential problem in drug discovery,
and previous approaches have achieved initial success. However, most existing
methods still suffer from invalid local structure or unrealistic conformation
issues, which are mainly due to the poor leaning of bond angles or torsional
angles. To alleviate these problems, we propose AUTODIFF, a diffusion-based
fragment-wise autoregressive generation model. Specifically, we design a novel
molecule assembly strategy named conformal motif that preserves the
conformation of local structures of molecules first, then we encode the
interaction of the protein-ligand complex with an SE(3)-equivariant
convolutional network and generate molecules motif-by-motif with diffusion
modeling. In addition, we also improve the evaluation framework of SBDD by
constraining the molecular weights of the generated molecules in the same
range, together with some new metrics, which make the evaluation more fair and
practical. Extensive experiments on CrossDocked2020 demonstrate that our
approach outperforms the existing models in generating realistic molecules with
valid structures and conformations while maintaining high binding affinity.

通过基于结构的药物设计方法和自动生成模型 AUTODIFF，以及分子组装策略 conformal motif 和 SE (3)- 等变卷积网络，本研究在 CrossDocked2020 的大范围实验中显示，相较于现有模型，我们的方法在生成具有有效结构和构象的真实分子的同时，并保持较高的结合亲和力方面取得了更好的性能。