Gast ::
| Hauptseite > Publikationsdatenbank > Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations > print |
| Hauptseite > Publikationsdatenbank > Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations > print |
| 001 | 1009524 | ||
| 005 | 20240625095124.0 | ||
| 024 | 7 | _ | |a 10.1021/acs.jcim.3c00557 |2 doi |
| 024 | 7 | _ | |a 0095-2338 |2 ISSN |
| 024 | 7 | _ | |a 1549-9596 |2 ISSN |
| 024 | 7 | _ | |a 1520-5142 |2 ISSN |
| 024 | 7 | _ | |a (BIS |2 ISSN |
| 024 | 7 | _ | |a 44.2004) |2 ISSN |
| 024 | 7 | _ | |a 1549-960X |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2023-02854 |2 datacite_doi |
| 024 | 7 | _ | |a 37319347 |2 pmid |
| 024 | 7 | _ | |a WOS:001020993900001 |2 WOS |
| 037 | _ | _ | |a FZJ-2023-02854 |
| 082 | _ | _ | |a 540 |
| 100 | 1 | _ | |a Raghavan, Bharath |0 P:(DE-Juel1)180693 |b 0 |e First author |
| 245 | _ | _ | |a Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations |
| 260 | _ | _ | |a Washington, DC |c 2023 |b American Chemical Society |
| 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 1694161994_5510 |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 |
| 500 | _ | _ | |a Grant name: DBA01838 Innovative high-performance computing applied to neurodegenerative diseasesOpen access publication |
| 520 | _ | _ | |a The initial phases of drug discovery – in silico drug design – could benefit from first principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, yet many applications are currently limited by the short time scales that this approach can cover. Developing scalable first principle QM/MM MD interfaces fully exploiting current exascale machines – so far an unmet and crucial goal – will help overcome this problem, opening the way to the study of the thermodynamics and kinetics of ligand binding to protein with first principle accuracy. Here, taking two relevant case studies involving the interactions of ligands with rather large enzymes, we showcase the use of our recently developed massively scalable Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework (currently using DFT to describe the QM region) to investigate reactions and ligand binding in enzymes of pharmacological relevance. We also demonstrate for the first time strong scaling of MiMiC-QM/MM MD simulations with parallel efficiency of ∼70% up to >80,000 cores. Thus, among many others, the MiMiC interface represents a promising candidate toward exascale applications by combining machine learning with statistical mechanics based algorithms tailored for exascale supercomputers. |
| 536 | _ | _ | |a 5241 - Molecular Information Processing in Cellular Systems (POF4-524) |0 G:(DE-HGF)POF4-5241 |c POF4-524 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Paulikat, Mirko |0 P:(DE-Juel1)181088 |b 1 |e First author |
| 700 | 1 | _ | |a Ahmad, Katya |0 P:(DE-Juel1)186082 |b 2 |
| 700 | 1 | _ | |a Callea, Lara |0 0000-0003-0071-601X |b 3 |
| 700 | 1 | _ | |a Rizzi, Andrea |0 P:(DE-Juel1)180791 |b 4 |
| 700 | 1 | _ | |a Ippoliti, Emiliano |0 P:(DE-Juel1)146009 |b 5 |
| 700 | 1 | _ | |a Mandelli, Davide |0 P:(DE-Juel1)190906 |b 6 |e Corresponding author |
| 700 | 1 | _ | |a Bonati, Laura |0 0000-0003-3028-0368 |b 7 |
| 700 | 1 | _ | |a De Vivo, Marco |0 P:(DE-Juel1)167585 |b 8 |
| 700 | 1 | _ | |a Carloni, Paolo |0 P:(DE-Juel1)145614 |b 9 |e Corresponding author |
| 773 | _ | _ | |a 10.1021/acs.jcim.3c00557 |g Vol. 63, no. 12, p. 3647 - 3658 |0 PERI:(DE-600)1491237-5 |n 12 |p 3647 - 3658 |t Journal of chemical information and modeling |v 63 |y 2023 |x 0095-2338 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1009524/files/acs.jcim.3c00557.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1009524 |p openaire |p open_access |p driver |p VDB |p openCost |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)180693 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)181088 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)180791 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)146009 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)190906 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 9 |6 P:(DE-Juel1)145614 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-524 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Molecular and Cellular Information Processing |9 G:(DE-HGF)POF4-5241 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 915 | p | c | |a APC keys set |0 PC:(DE-HGF)0000 |2 APC |
| 915 | p | c | |a Local Funding |0 PC:(DE-HGF)0001 |2 APC |
| 915 | p | c | |a DFG OA Publikationskosten |0 PC:(DE-HGF)0002 |2 APC |
| 915 | p | c | |a Helmholtz: American Chemical Society 01/01/2023 |0 PC:(DE-HGF)0122 |2 APC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2022-11-16 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2022-11-16 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b J CHEM INF MODEL : 2022 |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1150 |2 StatID |b Current Contents - Physical, Chemical and Earth Sciences |d 2023-10-21 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b J CHEM INF MODEL : 2022 |d 2023-10-21 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-5-20120330 |k IAS-5 |l Computational Biomedicine |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-9-20140121 |k INM-9 |l Computational Biomedicine |x 1 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-5-20120330 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-9-20140121 |
| 980 | _ | _ | |a APC |
| 980 | 1 | _ | |a APC |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|