上海科技大学知识管理系统

["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.","By incorporating the molecular conformational space profile into the contrastive pre-training, the authors have developed a molecular representation model called GeminiMol, which is trained on a small-scale molecular dataset but has a relatively strong generalization ability on large-scale benchmark datasets. GeminiMol shows excellent performance on multiple downstream tasks, including ligand-based virtual screening, target identification, and Quantitative Structure-Activity Relationship (QSAR) modeling, etc., thus providing a useful molecular representation tool for artificial intelligence-based drug design areas. image"]