Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.
Lin Wang, Shihang Wang, Hao Yang, Shiwei Li, Xinyu Wang, Yongqi Zhou, Siyuan Tian, Lu Liu, Fang Bai
Author Information
Lin Wang: Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, Shanghai Tech University, Shanghai, 201210, China. ORCID
Shihang Wang: Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, Shanghai Tech University, Shanghai, 201210, China.
Hao Yang: Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, Shanghai Tech University, Shanghai, 201210, China.
Shiwei Li: Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, Shanghai Tech University, Shanghai, 201210, China.
Xinyu Wang: Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, Shanghai Tech University, Shanghai, 201210, China.
Yongqi Zhou: Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, Shanghai Tech University, Shanghai, 201210, China.
Siyuan Tian: Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, Shanghai Tech University, Shanghai, 201210, China.
Lu Liu: Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, Shanghai Tech University, Shanghai, 201210, China.
Fang Bai: Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, Information Science and Technology, Shanghai Tech University, Shanghai Clinical Research and Trial Center, Shanghai, 201210, China. ORCID
The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. However, current molecular representation models rarely consider the three-dimensional conformational space of molecules, losing sight of the dynamic nature of small molecules as well as the essence of molecular conformational space that covers the heterogeneity of molecule properties, such as the multi-target mechanism of action, recognition of different biomolecules, dynamics in cytoplasm and membrane. In this study, a new model named GeminiMol is proposed to incorporate conformational space profiles into molecular representation learning, which extracts the feature of capturing the complicated interplay between the molecular structure and the conformational space. Although GeminiMol is pre-trained on a relatively small-scale molecular dataset (39290 molecules), it shows balanced and superior performance not only on 67 molecular properties predictions but also on 73 cellular activity predictions and 171 zero-shot tasks (including virtual screening and target identification). By capturing the molecular conformational space profile, the strategy paves the way for rapid exploration of chemical space and facilitates changing paradigms for drug design.
BMC Bioinformatics. 2010 Nov 04;11:545
[PMID: 21050454]
Protein Sci. 2007 May;16(5):897-905
[PMID: 17456742]
Bioinformatics. 2024 Jan 2;40(1):
[PMID: 38141210]
Grants
2022YFC3400501/National Key R&D Program of China
2022YFC3400500/National Key R&D Program of China
2020YFA0509700/National Key R&D Program of China
82341093/National Natural Science Foundation of China
82003654/National Natural Science Foundation of China
/Start-up package from ShanghaiTech University, and Shanghai Frontiers Science Center for Biomacromolecules and Precision Medicine at ShanghaiTech University
22ZR1441400/Shanghai Science and Technology Development Foundation
20QA1406400/Shanghai Science and Technology Development Foundation
