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 published in Nature Computational Science (2024). On theSindex it has a DataRank of 1.4, placing it in the top 38.6% of the data-sharing corpus. It has been cited 64 times, with 62 citing works in its 1-hop citation network. Its calibrated FAIR score is 53/100.
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.
›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
0.574
From this paper's citation signal
Citation Network Contribution
0.819
From 39 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
- NMRPipe: A multidimensional spectral processing system based on UNIX pipesJournal of Biomolecular NMR199516,271 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
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 41% comes from its base citations and 59% from the citation network (39 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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