ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction.Acta pharmaceutica Sinica. B · 2026Article
- Dual inhibition of KDM4B and KDM5A disassembles the PAX3-FOXO1 transcriptional program in fusion-positive rhabdomyosarcoma.Biology direct · 2026Article
- Spectral Decomposition of Chemical Semantics for Activity Cliffs-Aware Molecular Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Learned Conformational Space and Pharmacophore Into Molecular Foundational Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- PhenoModel: A multimodal phenotypic drug design foundation model for discovering novel potential inhibitors of multiple cancer cells.Acta pharmaceutica Sinica. B · 2026Article
- Antiviral drug discovery and development: challenges and future directions.Signal transduction and targeted therapy · 2026Review
- Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration.Chinese medicine · 2026Review
- Graph neural networks driven acceleration in drug discovery.Acta pharmaceutica Sinica. B · 2025Review
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Recent advances in molecular representation methods and their applications in scaffold hopping.npj drug discovery · 2025Review
- MAI-TargetFisher: A proteome-wide drug target prediction method synergetically enhanced by artificial intelligence and physical modeling.Acta pharmacologica Sinica · 2025Article
- Emerging technologies and current challenges in intratumoral microbiota research.Frontiers in cellular and infection microbiology · 2025Review
- Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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9 authors.
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Abstract
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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