ArticleNature computational science2024
MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery.
Article in Nature computational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling.Journal of chemical information and modeling · 2026Article
- PETIMOT: a novel framework for inferring protein motions from sparse data using SE(3)-equivariant graph neural networks.Acta crystallographica. Section D, Structural biology · 2026Article
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- Chemical neighborhood exploration for substrate discovery in biocatalysis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Enabling Automatic Generation of Protein-Ligand Complex Data Sets with Atomistic Detail.Journal of chemical information and modeling · 2026Article
- A Risk-Tiered Validation Framework for Artificial Intelligence in Drug Discovery: From Reproducibility to Clinical Translation.International journal of molecular sciences · 2026Review
- MDCompress: better, faster compression of molecular dynamics simulation trajectories.Bioinformatics (Oxford, England) · 2026Article
- Review
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- ViralBindPredict: empowering viral protein-ligand binding sites through deep learning and protein sequence-derived insights.GigaScience · 2026Article
- Structure-informed machine learning for drug discovery: a task-centric perspective.Briefings in bioinformatics · 2026Review
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2026Article
- Neg-entropy is the true drug target for chronic diseases.Acta pharmaceutica Sinica. B · 2026Review
- A multi-grained symmetric differential equation model for learning protein-ligand binding dynamics.Nature communications · 2025Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design.Chemical science · 2025Review
- An image-based protein-ligand binding representation learning framework via multi-level flexible dynamics trajectory pre-training.Bioinformatics (Oxford, England) · 2025Article
- Designing high-affinity 3D drug molecules via geometric spatial perception diffusion model.Briefings in bioinformatics · 2025Article
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13 authors.
Funding
Abstract
Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein-ligand complexes in explicit water is included, accumulating over 170 μs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models.
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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.