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.

翻译：基于结构的药物设计旨在生成能与靶蛋白紧密结合的分子，这是药物发现领域的关键问题，现有方法已取得初步成功。然而，多数现有方法仍存在局部结构无效或构象不真实的问题，这主要源于对键角或扭转角的学习不足。为缓解这些问题，我们提出AUTODIFF——一种基于扩散的片段式自回归生成模型。具体而言，我们设计了名为"共形基元"的新型分子组装策略，该策略首先保留分子局部结构的构象，进而通过SE(3)等变卷积网络编码蛋白质-配体复合物的相互作用，并利用扩散模型逐基元生成分子。此外，我们还通过约束生成分子在相同范围内的分子量并引入新评价指标，改进了SBDD的评价框架，使评估更为公平和实用。在CrossDocked2020数据集上的大量实验表明，本方法在保持高结合亲和力的同时，生成具有有效结构和构象的真实分子方面优于现有模型。