ArticleNature communications2024
A molecular video-derived foundation model for scientific drug discovery.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- Artificial Intelligence in Photodynamic Therapy: From Molecular Design of Photosensitizers to Intelligent Delivery, Adaptive Dosimetry, and Personalized Prognostication of Results.International journal of molecular sciences · 2026Review
- Scalable molecular representations enabled by multimodal fusion and sequence distillation.Nature communications · 2026Article
- Reimagining computational macromolecular modeling: AI-driven approaches.Biophysical journal · 2026Review
- MSF-HierGNN: a multi-source substructure-fusion hierarchical GNN method and web server to predict molecular property for drug design.Briefings in bioinformatics · 2026Article
- Empowering chemical structures with biological insights for scalable phenotypic virtual screening.Bioinformatics (Oxford, England) · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Foundation model for screening severe mitral regurgitation and severe aortic stenosis from coronary angiograms.Visual computing for industry, biomedicine, and art · 2026Article
- KAN-PROSPECT: a Kolmogorov-Arnold Networks-integrated framework for predicting the effects and adverse reactions of natural products via transfer learning.Scientific reports · 2026Article
- Revisiting ADMET prediction reliability under real-world challenges in the foundation model era.Journal of cheminformatics · 2026Article
- Multi-View Biomedical Foundation Models for Molecule-Target and Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- BiGvCL: bipartite graph-based cross-domain contrastive learning model for the predicting drug-gene interactions.Briefings in bioinformatics · 2026Article
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Artificial Intelligence to Guide Repurposing of Drugs.Annual review of medicine · 2026Review
- Network biology to artificial intelligence: building the next generation of predictive drug discovery.Frontiers in immunology · 2026Review
- Leveraging 3D Molecular Spatial Visual Information and Multi-Perspective Representations for Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multimodal pre-training models of molecular representation for drug discovery.National science review · 2026Review
- Translational Advances in Lipid Nanoparticle Drug Delivery Systems for Cancer Therapy: Current Status and Future Horizons.Pharmaceutics · 2025Review
- An image-based protein-ligand binding representation learning framework via multi-level flexible dynamics trajectory pre-training.Bioinformatics (Oxford, England) · 2025Article
- MaskMol: knowledge-guided molecular image pre-training framework for activity cliffs with pixel masking.BMC biology · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Accurate molecular representation of compounds is a fundamental challenge for prediction of drug targets and molecular properties. In this study, we present a molecular video-based foundation model, named VideoMol, pretrained on 120 million frames of 2 million unlabeled drug-like and bioactive molecules. VideoMol renders each molecule as a video with 60-frame and designs three self-supervised learning strategies on molecular videos to capture molecular representation. We show high performance of VideoMol in predicting molecular targets and properties across 43 drug discovery benchmark datasets. VideoMol achieves high accuracy in identifying antiviral molecules against common diverse disease-specific drug targets (i.e., BACE1 and EP4). Drugs screened by VideoMol show better binding affinity than molecular docking, revealing the effectiveness in understanding the three-dimensional structure of molecules. We further illustrate interpretability of VideoMol using key chemical substructures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.