MISATO - Machine learning dataset of protein-ligand complexes for structure-based drug discovery
MISATO - Machine learning dataset of protein-ligand complexes for structure-based drug discovery is a dataset (2023). On theSindex it has a DataRank of 1.0, placing it in the top 41.3% of the data-sharing corpus. It has been cited 23 times, with 22 citing works in its 1-hop citation network. Its calibrated FAIR score is 53/100.
Abstract
Large language models (LLMs) have greatly enhanced our ability to understand biology and chemistry. Yet, relatively few robust methods have been reported for structure-based drug discovery. Highly precise biomolecule-ligand interaction datasets are urgently needed in particular for LLMs, that require extensive training data. We present MISATO, the first dataset that combines quantum mechanics properties of small molecules and associated molecular dynamics simulations of about 20000 experimental protein-ligand complexes. Starting from the PDBbind dataset, semi-empirical quantum mechanics was used to systematically refine these structures. The largest collection to date of molecular dynamics traces of protein-ligand complexes in explicit water are included, accumulating to 170 μs. We give ML baseline models and simple Python data loaders, and aim to foster a thriving community around MISATO ( https://github.com/t7morgen/misato-dataset ). An easy entry point for ML experts is provided without the need of deep domain expertise to enable the next generation of drug discovery AI models.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.470
From this paper's citation signal
Citation Network Contribution
0.576
From 17 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Highly accurate protein structure prediction with AlphaFoldNature202143,672 citationsDataRank 1.6
- AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreadingJournal of Computational Chemistry200936,130 citationsDataRank 1.6
- Development and testing of a general amber force fieldJournal of Computational Chemistry200419,217 citationsDataRank 1.5
- Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular modelJournal of the American Chemical Society198513,091 citationsDataRank 1.4
- Open Babel: An open chemical toolboxJournal of Cheminformatics201110,787 citationsDataRank 1.4
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 45% comes from its base citations and 55% from the citation network (17 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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