Home > Publications database > Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective > print |
001 | 909641 | ||
005 | 20240625095123.0 | ||
024 | 7 | _ | |a 10.3389/fmolb.2022.899805 |2 doi |
024 | 7 | _ | |a 2128/31865 |2 Handle |
024 | 7 | _ | |a 35755817 |2 pmid |
024 | 7 | _ | |a WOS:000814363000001 |2 WOS |
037 | _ | _ | |a FZJ-2022-03312 |
041 | _ | _ | |a English |
082 | _ | _ | |a 570 |
100 | 1 | _ | |a Ahmad, Katya |0 P:(DE-Juel1)186082 |b 0 |u fzj |
245 | _ | _ | |a Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective |
260 | _ | _ | |a Lausanne |c 2022 |b Frontiers |
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 1663672680_27718 |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 The dissociation rate (koff) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of koff. Next, we discuss the impact of the potential energy function models on the accuracy of calculated koff values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions. |
536 | _ | _ | |a 1215 - Simulations, Theory, Optics, and Analytics (STOA) (POF4-121) |0 G:(DE-HGF)POF4-1215 |c POF4-121 |f POF IV |x 0 |
536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
650 | 2 | 7 | |a Condensed Matter Physics |0 V:(DE-MLZ)SciArea-120 |2 V:(DE-HGF) |x 0 |
650 | 2 | 7 | |a Others |0 V:(DE-MLZ)SciArea-250 |2 V:(DE-HGF) |x 1 |
650 | 1 | 7 | |a Basic research |0 V:(DE-MLZ)GC-2004-2016 |2 V:(DE-HGF) |x 0 |
700 | 1 | _ | |a Rizzi, Andrea |0 P:(DE-Juel1)180791 |b 1 |u fzj |
700 | 1 | _ | |a Capelli, Riccardo |0 P:(DE-Juel1)174546 |b 2 |
700 | 1 | _ | |a Mandelli, Davide |0 P:(DE-Juel1)190906 |b 3 |u fzj |
700 | 1 | _ | |a Lyu, Wenping |0 P:(DE-Juel1)180596 |b 4 |
700 | 1 | _ | |a Carloni, Paolo |0 P:(DE-Juel1)145614 |b 5 |e Corresponding author |
773 | _ | _ | |a 10.3389/fmolb.2022.899805 |g Vol. 9, p. 899805 |0 PERI:(DE-600)2814330-9 |p 899805 |t Frontiers in molecular biosciences |v 9 |y 2022 |x 2296-889X |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/909641/files/fmolb-09-899805.pdf |y OpenAccess |
909 | C | O | |o oai:juser.fz-juelich.de:909641 |p openaire |p open_access |p OpenAPC |p driver |p VDB |p ec_fundedresources |p openCost |p dnbdelivery |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)186082 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)180791 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)190906 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)145614 |
913 | 1 | _ | |a DE-HGF |b Forschungsbereich Energie |l Materialien und Technologien für die Energiewende (MTET) |1 G:(DE-HGF)POF4-120 |0 G:(DE-HGF)POF4-121 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-100 |4 G:(DE-HGF)POF |v Photovoltaik und Windenergie |9 G:(DE-HGF)POF4-1215 |x 0 |
914 | 1 | _ | |y 2022 |
915 | p | c | |a Local Funding |2 APC |0 PC:(DE-HGF)0001 |
915 | p | c | |a DFG OA Publikationskosten |2 APC |0 PC:(DE-HGF)0002 |
915 | p | c | |a DOAJ Journal |2 APC |0 PC:(DE-HGF)0003 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2020-08-18 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2020-08-18 |
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 2020-08-18 |
915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2020-08-18 |
915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2020-08-18 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2022-11-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2022-11-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2021-05-11T12:25:52Z |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2021-05-11T12:25:52Z |
915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Blind peer review |d 2021-05-11T12:25:52Z |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2022-11-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2022-11-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2022-11-22 |
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 | 1 | _ | |a FullTexts |
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 |
Library | Collection | CLSMajor | CLSMinor | Language | Author |
---|