药物发现
仿形(计算机编程)
化学空间
计算机科学
药品
代表(政治)
组合化学
计算生物学
化学
纳米技术
材料科学
医学
药理学
生物化学
生物
政治
政治学
法学
操作系统
作者
Lin Wang,Shihang Wang,Hao Yang,Shiwei Li,Xinyu Wang,Yongqi Zhou,Siyuan Tian,Lu Liu,Fang Bai
标识
DOI:10.1002/advs.202403998
摘要
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.
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