TY - JOUR
T1 - MolCRAFT
T2 - 41st International Conference on Machine Learning, ICML 2024
AU - Qu, Yanru
AU - Qiu, Keyue
AU - Song, Yuxuan
AU - Gong, Jingjing
AU - Han, Jiawei
AU - Zheng, Mingyue
AU - Zhou, Hao
AU - Ma, Wei Ying
N1 - This work is supported by the National Science and Technology Major Project (2022ZD0117502), Natural Science Foundation of China (62376133) and Guoqiang Research Institute General Project, Tsinghua University (No. 2021GQG1012). Jiawei Han's work is supported by U.S. National Science Foundation IIS-19-56151, and the Molecule Maker Lab Institute: An AI Research Institutes program supported by U.S. NSF under Award No. 2019897. The authors would like to thank Xiangyu Li for his valuable comments on this work.
PY - 2024
Y1 - 2024
N2 - 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 MolCRAFT, 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) with comparable molecular size, outperforming other strong baselines by a wide margin (-0.84 kcal/mol). Code is available at https://github.com/AlgoMole/MolCRAFT.
AB - 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 MolCRAFT, 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) with comparable molecular size, outperforming other strong baselines by a wide margin (-0.84 kcal/mol). Code is available at https://github.com/AlgoMole/MolCRAFT.
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M3 - Conference article
AN - SCOPUS:85203821079
SN - 2640-3498
VL - 235
SP - 41749
EP - 41768
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
Y2 - 21 July 2024 through 27 July 2024
ER -