TY  - JOUR
AU  - Siebenmorgen, Till
AU  - Menezes, Filipe
AU  - Benassou, Sabrina
AU  - Merdivan, Erinc
AU  - Didi, Kieran
AU  - Mourão, André Santos Dias
AU  - Kitel, Radosław
AU  - Liò, Pietro
AU  - Kesselheim, Stefan
AU  - Piraud, Marie
AU  - Theis, Fabian J.
AU  - Sattler, Michael
AU  - Popowicz, Grzegorz M.
TI  - MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery
JO  - Nature computational science
VL  - 4
IS  - 5
SN  - 2662-8457
CY  - London
PB  - Nature Research
M1  - FZJ-2025-00819
SP  - 367 - 378
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 38730184
UR  - <Go to ISI:>//WOS:001220857400002
DO  - DOI:10.1038/s43588-024-00627-2
UR  - https://juser.fz-juelich.de/record/1037654
ER  -