Gast ::
| Hauptseite > Publikationsdatenbank > MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery > print |
| Hauptseite > Publikationsdatenbank > MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery > print |
| 001 | 1037654 | ||
| 005 | 20250203124502.0 | ||
| 024 | 7 | _ | |a 10.1038/s43588-024-00627-2 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-00819 |2 datacite_doi |
| 024 | 7 | _ | |a 38730184 |2 pmid |
| 024 | 7 | _ | |a WOS:001220857400002 |2 WOS |
| 037 | _ | _ | |a FZJ-2025-00819 |
| 082 | _ | _ | |a 004 |
| 100 | 1 | _ | |a Siebenmorgen, Till |0 0009-0008-5160-8100 |b 0 |
| 245 | _ | _ | |a MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery |
| 260 | _ | _ | |a London |c 2024 |b Nature Research |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1737441865_21954 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Menezes, Filipe |0 0000-0002-7630-5447 |b 1 |
| 700 | 1 | _ | |a Benassou, Sabrina |0 P:(DE-Juel1)192312 |b 2 |u fzj |
| 700 | 1 | _ | |a Merdivan, Erinc |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Didi, Kieran |0 0000-0001-6839-3320 |b 4 |
| 700 | 1 | _ | |a Mourão, André Santos Dias |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Kitel, Radosław |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Liò, Pietro |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Kesselheim, Stefan |0 P:(DE-Juel1)185654 |b 8 |
| 700 | 1 | _ | |a Piraud, Marie |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Theis, Fabian J. |0 0000-0002-2419-1943 |b 10 |
| 700 | 1 | _ | |a Sattler, Michael |0 0000-0002-1594-0527 |b 11 |
| 700 | 1 | _ | |a Popowicz, Grzegorz M. |0 0000-0003-2818-7498 |b 12 |e Corresponding author |
| 773 | _ | _ | |a 10.1038/s43588-024-00627-2 |g Vol. 4, no. 5, p. 367 - 378 |0 PERI:(DE-600)3029424-1 |n 5 |p 367 - 378 |t Nature computational science |v 4 |y 2024 |x 2662-8457 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1037654/files/s43588-024-00627-2.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1037654 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)192312 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 8 |6 P:(DE-Juel1)185654 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2024-12-13 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b NAT COMPUT SCI : 2022 |d 2024-12-13 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0112 |2 StatID |b Emerging Sources Citation Index |d 2024-12-13 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2024-12-13 |
| 915 | _ | _ | |a DEAL Nature |0 StatID:(DE-HGF)3003 |2 StatID |d 2024-12-13 |w ger |
| 915 | _ | _ | |a IF >= 10 |0 StatID:(DE-HGF)9910 |2 StatID |b NAT COMPUT SCI : 2022 |d 2024-12-13 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2024-12-13 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2024-12-13 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|