Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency1–3. Classical molecular dynamics simulation is fast but lacks chemical accuracy4,5. Quantum chemistry methods such as density functional theory can reach chemical accuracy but cannot scale to support large biomolecules6. Here we introduce an artificial intelligence-based ab initio biomolecular dynamics system (AI2BMD) that can efficiently simulate full-atom large biomolecules with ab initio accuracy. AI2BMD uses a protein fragmentation scheme and a machine learning force field7 to achieve generalizable ab initio accuracy for energy and force calculations for various proteins comprising more than 10,000 atoms. Compared to density functional theory, it reduces the computational time by several orders of magnitude. With several hundred nanoseconds of dynamics simulations, AI2BMD demonstrated its ability to efficiently explore the conformational space of peptides and proteins, deriving accurate 3J couplings that match nuclear magnetic resonance experiments, and showing protein folding and unfolding processes. Furthermore, AI2BMD enables precise free-energy calculations for protein folding, and the estimated thermodynamic properties are well aligned with experiments. AI2BMD could potentially complement wet-lab experiments, detect the dynamic processes of bioactivities and enable biomedical research that is impossible to conduct at present.
@article{Wang2024,author={Wang, Tong and He, Xinheng and Li, Mingyu and Li, Yatao and Bi, Ran and Wang, Yusong and Cheng, Chaoran and Shen, Xiangzhen an d Meng, Jiawei and Zhang, He and Liu, Haiguang and Wang, Zun and Li, Shaoning and Shao, Bin and Liu, Tie-Yan},title={Ab initio characterization of protein molecular dynamics with AI2BMD},journal={Nature},year={2024},month=nov,day={06},issn={1476-4687},doi={10.1038/s41586-024-08127-z},url={https://doi.org/10.1038/s41586-024-08127-z},}
Categorical Flow Matching on Statistical Manifolds
Chaoran Cheng, Jiahan Li, Jian Peng, and Ge Liu
In Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024 Nov 2024
@inproceedings{cheng2024categorical,title={Categorical Flow Matching on Statistical Manifolds},author={Cheng, Chaoran and Li, Jiahan and Peng, Jian and Liu, Ge},year={2024},booktitle={Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024},}
Neural P³M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
Yusong Wang, Chaoran Cheng, Shaoning Li, Yuxuan Ren, Bin Shao, Ge Liu, Pheng-Ann Heng, and Nanning Zheng
In Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024 Nov 2024
@inproceedings{wang2024neuralp3m,title={Neural P³M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs},author={Wang, Yusong and Cheng, Chaoran and Li, Shaoning and Ren, Yuxuan and Shao, Bin and Liu, Ge and Heng, Pheng-Ann and Zheng, Nanning},year={2024},booktitle={Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024},}
Full-Atom Peptide Design based on Multi-modal Flow Matching
Jiahan Li, Chaoran Cheng, Zuofan Wu, Ruihan Guo, Shitong Luo, Zhizhou Ren, Jian Peng, and Jianzhu Ma
In Forty-first International Conference on Machine Learning, ICML 2024 Nov 2024
@inproceedings{li2024fullatom,title={Full-Atom Peptide Design based on Multi-modal Flow Matching},booktitle={Forty-first International Conference on Machine Learning, {ICML} 2024},author={Li, Jiahan and Cheng, Chaoran and Wu, Zuofan and Guo, Ruihan and Luo, Shitong and Ren, Zhizhou and Peng, Jian and Ma, Jianzhu},series={Proceedings of Machine Learning Research},publisher={{PMLR}},year={2024},}
2023
Equivariant Neural Operator Learning with Graphon Convolution
Chaoran Cheng, and Jian Peng
In Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023 Nov 2023
@inproceedings{Cheng2023infgcn,title={Equivariant Neural Operator Learning with Graphon Convolution},author={Cheng, Chaoran and Peng, Jian},booktitle={Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023},year={2023},}
2022
Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
@inproceedings{Luo2022CVPR,author={Luo, Shitong and Li, Jiahan and Guan, Jiaqi and Su, Yufeng and Cheng, Chaoran and Peng, Jian and Ma, Jianzhu},title={Equivariant Point Cloud Analysis via Learning Orientations for Message Passing},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month=jun,year={2022},pages={18932-18941},}
DisenCite: Graph-Based Disentangled Representation Learning for Context-Specific Citation Generation
Yifan Wang, Yiping Song, Shuai Li, Chaoran Cheng, Wei Ju, Ming Zhang, and Sheng Wang
Proceedings of the AAAI Conference on Artificial Intelligence Jun 2022
@article{IJCAI2022disen,title={DisenCite: Graph-Based Disentangled Representation Learning for Context-Specific Citation Generation},volume={36},url={https://ojs.aaai.org/index.php/AAAI/article/view/21397},doi={10.1609/aaai.v36i10.21397},number={10},journal={Proceedings of the AAAI Conference on Artificial Intelligence},author={Wang, Yifan and Song, Yiping and Li, Shuai and Cheng, Chaoran and Ju, Wei and Zhang, Ming and Wang, Sheng},year={2022},month=jun,pages={11449-11458},}
Orientation-Aware Graph Neural Networks for Protein Structure Representation LearningarXiv Nov 2022
