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，是药物发现中的关键问题，现有方法已取得初步成功。然而，大多数现有方法仍存在无效局部结构或非真实构象的问题，这主要源于对键角或扭转角的学习不足。为解决这些问题，我们提出了AUTODIFF——一种基于扩散的片段式自回归生成模型。具体而言，我们设计了一种名为"构象模体"的新型分子组装策略，该策略优先保留分子局部结构的构象，随后利用SE(3)-等变卷积网络编码蛋白质-配体复合物之间的相互作用，并通过扩散建模逐模体生成分子。此外，我们通过将生成分子的分子量约束在同一范围内，并结合新引入的评估指标，改进了SBDD的评估框架，使评估更加公平和实用。在CrossDocked2020数据集上的大量实验表明，我们的方法在生成具有有效结构和真实构象且保持高结合亲和力的分子方面优于现有模型。