MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space
Yanru Qu Keyue Qiu Yuxuan Song Jingjing Gong Jiawei Han Mingyue Zheng Hao Zhou Wei-Ying Ma
Generative models for structure-based drug design (SBDD) have shown promising
results in recent years. Existing works mainly focus on how to generate
molecules with higher binding affinity, ignoring the feasibility prerequisites
for generated 3D poses and resulting in false positives. We conduct thorough
studies on key factors of ill-conformational problems when applying
autoregressive methods and diffusion to SBDD, including mode collapse and
hybrid continuous-discrete space. In this paper, we introduce \ours, the first
SBDD model that operates in the continuous parameter space, together with a
novel noise reduced sampling strategy. Empirical results show that our model
consistently achieves superior performance in binding affinity with more stable
3D structure, demonstrating our ability to accurately model interatomic
interactions. To our best knowledge, MolCRAFT is the first to achieve
reference-level Vina Scores (-6.59 kcal/mol), outperforming other strong
baselines by a wide margin (-0.84 kcal/mol). Code is available at
https://github.com/AlgoMole/MolCRAFT.