Improving ligand‐ranking of AutoDock Vina by changing the empirical parameters

AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations....

Full description

Saved in:

Bibliographic Details
Published in:	Journal of computational chemistry Vol. 43; no. 3; pp. 160 - 169
Main Authors:	Pham, T. Ngoc Han, Nguyen, Trung Hai, Tam, Nguyen Minh, Y. Vu, Thien, Pham, Nhat Truong, Huy, Nguyen Truong, Mai, Binh Khanh, Tung, Nguyen Thanh, Pham, Minh Quan, V. Vu, Van, Ngo, Son Tung
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 30.01.2022 Wiley Subscription Services, Inc
Subjects:	Affinity AutoDock Vina Binding binding affinity Correlation coefficients Docking empirical parameter Hydrogen bonds Ligands Mathematical analysis modified Vina Parameters Ranking screening binding affinity screening empirical parameter AutoDock Vina modified Vina
ISSN:	0192-8651, 1096-987X, 1096-987X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking‐success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (Rset1=0.617±0.017) than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand‐binding affinity using Autodock Vina. A new set of empirical parameters of AutoDock Vina was proposed. The accuracy of affinity prediction was significantly increased from RDefault=0.493±0.028 to Rset1=0.556±0.025 over 800 testing complexes. Over 1036 validating complexes, the proposed parameter formed Rset1=0.617±0.017, which is rigidly larger than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020).
AbstractList	AutoDock Vina (Vina) achieved a very high docking-success rate, p^ , but give a rather low correlation coefficient, R , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( Rset1=0.617±0.017 ) than the default package ( RDefault=0.543±0.020 ) and Vina version 1.2 ( RVina1.2=0.540±0.020 ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.AutoDock Vina (Vina) achieved a very high docking-success rate, p^ , but give a rather low correlation coefficient, R , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( Rset1=0.617±0.017 ) than the default package ( RDefault=0.543±0.020 ) and Vina version 1.2 ( RVina1.2=0.540±0.020 ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina. AutoDock Vina (Vina) achieved a very high docking‐success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. is affected more by changing the gauss2 and rotation than other terms. The docking‐success rate is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment compared with obtained by the original Vina and by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving for 32/48 targets, compared with the default package, giving for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( ) than the default package ( ) and Vina version 1.2 ( ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina . The outcomes would enhance the ranking of ligand‐binding affinity using Autodock Vina. AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking‐success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (Rset1=0.617±0.017) than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand‐binding affinity using Autodock Vina. A new set of empirical parameters of AutoDock Vina was proposed. The accuracy of affinity prediction was significantly increased from RDefault=0.493±0.028 to Rset1=0.556±0.025 over 800 testing complexes. Over 1036 validating complexes, the proposed parameter formed Rset1=0.617±0.017, which is rigidly larger than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020). AutoDock Vina (Vina) achieved a very high docking-success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. is affected more by changing the gauss2 and rotation than other terms. The docking-success rate is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment compared with obtained by the original Vina and by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving for 32/48 targets, compared with the default package, giving for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( ) than the default package ( ) and Vina version 1.2 ( ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina. AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking‐success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (Rset1=0.617±0.017) than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand‐binding affinity using Autodock Vina.
Author	V. Vu, Van Nguyen, Trung Hai Huy, Nguyen Truong Mai, Binh Khanh Pham, Minh Quan Ngo, Son Tung Tam, Nguyen Minh Pham, Nhat Truong Pham, T. Ngoc Han Tung, Nguyen Thanh Y. Vu, Thien
Author_xml	– sequence: 1 givenname: T. Ngoc Han surname: Pham fullname: Pham, T. Ngoc Han organization: Ton Duc Thang University – sequence: 2 givenname: Trung Hai orcidid: 0000-0003-1848-3963 surname: Nguyen fullname: Nguyen, Trung Hai organization: Ton Duc Thang University – sequence: 3 givenname: Nguyen Minh orcidid: 0000-0003-3153-4606 surname: Tam fullname: Tam, Nguyen Minh organization: Ton Duc Thang University – sequence: 4 givenname: Thien surname: Y. Vu fullname: Y. Vu, Thien organization: Ton Duc Thang University – sequence: 5 givenname: Nhat Truong orcidid: 0000-0002-8086-6722 surname: Pham fullname: Pham, Nhat Truong organization: Ton Duc Thang University – sequence: 6 givenname: Nguyen Truong surname: Huy fullname: Huy, Nguyen Truong organization: Ton Duc Thang University – sequence: 7 givenname: Binh Khanh orcidid: 0000-0001-8487-1417 surname: Mai fullname: Mai, Binh Khanh organization: University of Pittsburgh – sequence: 8 givenname: Nguyen Thanh orcidid: 0000-0003-0232-7261 surname: Tung fullname: Tung, Nguyen Thanh organization: Vietnam Academy of Science and Technology – sequence: 9 givenname: Minh Quan orcidid: 0000-0001-6922-1627 surname: Pham fullname: Pham, Minh Quan organization: Vietnam Academy of Science and Technology – sequence: 10 givenname: Van orcidid: 0000-0003-0009-6703 surname: V. Vu fullname: V. Vu, Van organization: Nguyen Tat Thanh University – sequence: 11 givenname: Son Tung orcidid: 0000-0003-1034-1768 surname: Ngo fullname: Ngo, Son Tung email: ngosontung@tdtu.edu.vn organization: Ton Duc Thang University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/34716930$$D View this record in MEDLINE/PubMed
BookMark	eNp1kc1u1DAUhS3Uik4LC14ARWJDF2n9F8deVgOlRZVgAYiddce5mXqa2IOdgGbHI_CMPEkzzHRTldWVzv3O0dU9x-QgxICEvGL0jFHKz1fOnXFV1-YZmTFqVGl0_f2AzCgzvNSqYkfkOOcVpVRUSj4nR0LWTBlBZ-Tzdb9O8acPy6LzSwjN399_EoS7rRDb4mIc4rvo7opvPkCx2BTuFsJyuxxuscB-7ZN30BVrSNDjgCm_IIctdBlf7ucJ-Xr5_sv8qrz59OF6fnFTOlEJU2poAVFVgkkNctE0rtWicbrhnGsASU29kA1tqZ4EVJrJqq5QIzPY1pxKcULe7nKn83-MmAfb--yw6yBgHLPllaFMqEqKCX3zCF3FMYXpOssVramRhm0DX--pcdFjY9fJ95A29uFXE3C-A1yKOSdsrfMDDD6GIYHvLKN224ad2rD_2pgcp48cD6FPsfv0X77Dzf9B-3E-3znuAfyRmH8
CitedBy_id	crossref_primary_10_1007_s44371_025_00246_4 crossref_primary_10_1039_D5RA00709G crossref_primary_10_1016_j_jep_2022_115138 crossref_primary_10_3390_molecules27217241 crossref_primary_10_1016_j_molstruc_2025_143099 crossref_primary_10_1080_07391102_2023_2175382 crossref_primary_10_1080_14786419_2025_2528973 crossref_primary_10_1002_slct_202400404 crossref_primary_10_1016_j_cplett_2024_141553 crossref_primary_10_1016_j_bpc_2024_107357 crossref_primary_10_1098_rsos_240546 crossref_primary_10_3390_ph17050637 crossref_primary_10_1039_D4RA00692E crossref_primary_10_1080_14786419_2024_2425045 crossref_primary_10_1007_s11030_023_10601_1 crossref_primary_10_1016_j_ecoenv_2022_113323 crossref_primary_10_1016_j_jep_2022_115624 crossref_primary_10_1039_D5RA00488H crossref_primary_10_1039_D5RA02615F crossref_primary_10_1039_D4RA02661F crossref_primary_10_1007_s11030_025_11174_x crossref_primary_10_1007_s00044_025_03447_9 crossref_primary_10_1080_07391102_2022_2118830 crossref_primary_10_1016_j_biopha_2025_118252 crossref_primary_10_1016_j_biopha_2024_116974 crossref_primary_10_1007_s12272_025_01550_4 crossref_primary_10_3390_ijms24021280 crossref_primary_10_1002_cbdv_202500907 crossref_primary_10_1109_TBCAS_2024_3388323 crossref_primary_10_1002_cbdv_202501571 crossref_primary_10_1080_07391102_2024_2321509 crossref_primary_10_1039_D5CP01353D crossref_primary_10_3390_ijms25147724 crossref_primary_10_1080_14786419_2024_2305659 crossref_primary_10_1016_j_molstruc_2024_141104 crossref_primary_10_1016_j_jmgm_2024_108906 crossref_primary_10_1080_10942912_2023_2185178 crossref_primary_10_1002_aoc_70215
Cites_doi	10.1038/s41467-019-08646-8 10.1016/S0040-4039(01)94977-9 10.1016/j.softx.2015.06.001 10.1039/D1RA02529E 10.1093/nar/gkv951 10.1039/D0RA10529E 10.1063/1.5017136 10.1021/ar000033j 10.1021/acs.jctc.0c00634 10.1038/nprot.2016.051 10.1002/jcc.21334 10.1016/j.jmgm.2019.06.008 10.1021/jm030644s 10.1006/jmbi.1996.0897 10.1039/C9RA09583G 10.1039/D0RA09858B 10.1021/acs.chemrev.5b00630 10.1074/jbc.RA119.009509 10.1063/1.1749657 10.1021/acs.jpcb.8b03277 10.1021/acsomega.8b03258 10.1214/aos/1176344552 10.1002/jcc.26439 10.1002/wcms.1455 10.1073/pnas.1805020115 10.1002/jcc.21256 10.1007/978-1-61779-465-0_20 10.1007/s11030-016-9717-4 10.1021/acs.jcim.1c00203 10.1063/1.1740409 10.1088/2399-6528/abcbac 10.1021/acs.jcim.5b00559 10.1002/qsar.19950140602 10.1063/1.3119261 10.1146/annurev.pa.27.040187.001205 10.1038/s41598-020-57781-6 10.1103/PhysRevE.56.5018 10.1021/ct1001768 10.3390/ijms140612157 10.1021/acs.jcim.1c00159 10.1021/ci500301s 10.1007/978-1-4939-6634-9_5 10.1023/A:1007930623000 10.1124/pr.112.007336 10.1038/nchembio.1697 10.1002/prot.22543 10.1038/s12276-019-0205-7 10.1021/ja9738539 10.1016/j.cplett.2017.03.034 10.1021/jm0306430 10.1002/jcc.24502 10.1016/0040-4020(80)80168-2 10.1021/cr00023a004 10.1021/acs.chemrev.0c00534 10.1016/j.jmgm.2019.107441 10.1021/acs.jcim.8b00312 10.1021/acs.jcim.9b00778 10.1002/jcc.25139 10.1002/pro.3784 10.1002/jcc.26130 10.1021/jm000241h 10.1021/acs.jcim.0c00491 10.1039/C9RA01981B
ContentType	Journal Article
Copyright	2021 Wiley Periodicals LLC. 2022 Wiley Periodicals LLC.
Copyright_xml	– notice: 2021 Wiley Periodicals LLC. – notice: 2022 Wiley Periodicals LLC.
DBID	AAYXX CITATION NPM JQ2 7X8
DOI	10.1002/jcc.26779
DatabaseName	CrossRef PubMed ProQuest Computer Science Collection MEDLINE - Academic
DatabaseTitle	CrossRef PubMed ProQuest Computer Science Collection MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic CrossRef PubMed ProQuest Computer Science Collection
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Chemistry
EISSN	1096-987X
EndPage	169
ExternalDocumentID	34716930 10_1002_jcc_26779 JCC26779
Genre	article Research Support, Non-U.S. Gov't Journal Article
GroupedDBID	--- -~X .3N .GA 05W 0R~ 10A 1L6 1OB 1OC 1ZS 33P 36B 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5GY 5VS 66C 6P2 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AAHQN AAMNL AANLZ AAONW AAXRX AAYCA AAZKR ABCQN ABCUV ABIJN ABJNI ABLJU ABPVW ACAHQ ACCFJ ACCZN ACFBH ACGFO ACGFS ACIWK ACNCT ACPOU ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN AEEZP AEGXH AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFWVQ AFZJQ AHBTC AIAGR AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ATUGU AUFTA AZBYB AZVAB BAFTC BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CS3 D-E D-F DCZOG DPXWK DR1 DR2 DRFUL DRSTM DU5 EBS ESX F00 F01 F04 F5P G-S G.N GNP GODZA H.T H.X HBH HGLYW HHY HHZ HZ~ IX1 J0M JPC KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG P2P P2W P2X P4D PQQKQ Q.N Q11 QB0 QRW R.K RNS ROL RWI RWK RX1 RYL SUPJJ TN5 UB1 UPT V2E V8K W8V W99 WBFHL WBKPD WH7 WIB WIH WIK WJL WOHZO WQJ WRC WXSBR WYISQ XG1 XPP XV2 YQT ZZTAW ~IA ~KM ~WT AAMMB AAYXX ADMLS AEFGJ AEYWJ AGHNM AGXDD AGYGG AIDQK AIDYY CITATION O8X NPM JQ2 7X8
ID	FETCH-LOGICAL-c3539-8afaee653148a4bddcf83dc8d2228aa4097b4d0f08222e6814575e8e19ef72043
IEDL.DBID	DRFUL
ISICitedReferencesCount	55
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000712877900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0192-8651 1096-987X
IngestDate	Fri Jul 11 15:10:34 EDT 2025 Fri Jul 25 18:56:51 EDT 2025 Mon Jul 21 05:25:47 EDT 2025 Sat Nov 29 03:23:43 EST 2025 Tue Nov 18 21:48:58 EST 2025 Wed Jan 22 16:28:13 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	3
Keywords	binding affinity screening empirical parameter AutoDock Vina modified Vina
Language	English
License	2021 Wiley Periodicals LLC.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c3539-8afaee653148a4bddcf83dc8d2228aa4097b4d0f08222e6814575e8e19ef72043
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-8086-6722 0000-0003-0009-6703 0000-0003-1848-3963 0000-0003-3153-4606 0000-0003-1034-1768 0000-0003-0232-7261 0000-0001-8487-1417 0000-0001-6922-1627
PMID	34716930
PQID	2607094914
PQPubID	48816
PageCount	10
ParticipantIDs	proquest_miscellaneous_2590136543 proquest_journals_2607094914 pubmed_primary_34716930 crossref_citationtrail_10_1002_jcc_26779 crossref_primary_10_1002_jcc_26779 wiley_primary_10_1002_jcc_26779_JCC26779
PublicationCentury	2000
PublicationDate	January 30, 2022
PublicationDateYYYYMMDD	2022-01-30
PublicationDate_xml	– month: 01 year: 2022 text: January 30, 2022 day: 30
PublicationDecade	2020
PublicationPlace	Hoboken, USA
PublicationPlace_xml	– name: Hoboken, USA – name: United States – name: New York
PublicationTitle	Journal of computational chemistry
PublicationTitleAlternate	J Comput Chem
PublicationYear	2022
Publisher	John Wiley & Sons, Inc Wiley Subscription Services, Inc
Publisher_xml	– name: John Wiley & Sons, Inc – name: Wiley Subscription Services, Inc
References	2018; 122 2019; 91 2015; 1–2 2019; 93 2019; 51 2020; 120 2019; 10 2000; 43 2020; 60 2020; 16 2020; 10 2016; 37 2017; 676 2014; 66 2018; 47 1997; 267 2018; 39 1980; 36 2020; 4 2013; 14 1997; 56 2016; 116 1998; 12 2010; 6 2014; 11 2014; 54 1998; 120 2001; 98 2016; 44 1979; 7 2010; 78 2010; 31 2019; 9 2019; 4 2021; 42 2012 1954; 22 2020; 41 1995; 14 2004; 47 2017; 21 2018; 148 2015; 55 1978; 19 2009; 130 2016; 18 2016; 11 2009; 30 2021; 11 1993; 93 2000; 33 2018; 115 2017 2019; 294 2021; 61 1935; 3 2018; 58 1987; 27 2020; 29 e_1_2_6_51_1 e_1_2_6_53_1 e_1_2_6_32_1 e_1_2_6_30_1 e_1_2_6_19_1 e_1_2_6_13_1 e_1_2_6_36_1 e_1_2_6_59_1 e_1_2_6_11_1 e_1_2_6_34_1 e_1_2_6_17_1 e_1_2_6_55_1 Ngo S. T. (e_1_2_6_40_1) 2020; 10 e_1_2_6_15_1 e_1_2_6_38_1 e_1_2_6_57_1 e_1_2_6_62_1 e_1_2_6_64_1 e_1_2_6_20_1 e_1_2_6_41_1 e_1_2_6_60_1 Wang W. (e_1_2_6_29_1) 2001; 98 e_1_2_6_9_1 Wang Z. (e_1_2_6_68_1) 2016; 18 e_1_2_6_5_1 e_1_2_6_7_1 e_1_2_6_24_1 e_1_2_6_49_1 e_1_2_6_3_1 e_1_2_6_22_1 e_1_2_6_66_1 e_1_2_6_28_1 e_1_2_6_45_1 e_1_2_6_26_1 e_1_2_6_52_1 e_1_2_6_54_1 e_1_2_6_10_1 e_1_2_6_31_1 e_1_2_6_50_1 Yu W. (e_1_2_6_6_1) 2017 e_1_2_6_14_1 e_1_2_6_35_1 e_1_2_6_12_1 e_1_2_6_33_1 e_1_2_6_18_1 e_1_2_6_39_1 e_1_2_6_56_1 e_1_2_6_16_1 e_1_2_6_37_1 e_1_2_6_58_1 e_1_2_6_63_1 e_1_2_6_42_1 e_1_2_6_65_1 e_1_2_6_21_1 Mendez D. (e_1_2_6_47_1) 2018; 47 e_1_2_6_61_1 Pham M. Q. (e_1_2_6_43_1) 2020; 10 e_1_2_6_8_1 e_1_2_6_4_1 e_1_2_6_25_1 e_1_2_6_48_1 e_1_2_6_23_1 e_1_2_6_2_1 e_1_2_6_44_1 e_1_2_6_67_1 e_1_2_6_27_1 e_1_2_6_46_1 e_1_2_6_69_1
References_xml	– volume: 56 start-page: 5018 issue: 5 year: 1997 publication-title: Phys. Rev. E – volume: 6 start-page: 2559 issue: 9 year: 2010 publication-title: J. Chem. Theory Comput. – volume: 27 start-page: 193 year: 1987 publication-title: Annu. Rev. Pharmacol. Toxicol. – volume: 42 start-page: 117 year: 2021 publication-title: J. Comput. Chem. – volume: 47 start-page: 1750 issue: 7 year: 2004 publication-title: J. Med. Chem. – volume: 55 start-page: 2324 issue: 11 year: 2015 publication-title: J. Chem. Inf. Model. – volume: 115 start-page: 7786 issue: 33 year: 2018 publication-title: Proc. Natl. Acad. Sci. U. S. A. – volume: 66 start-page: 334 issue: 1 year: 2014 publication-title: Pharmacol. Rev. – volume: 1–2 start-page: 19 year: 2015 publication-title: SoftwareX – volume: 37 start-page: 2734 issue: 31 year: 2016 publication-title: J. Comput. Chem. – volume: 93 year: 2019 publication-title: J. Mol. Graph. Model. – volume: 60 start-page: 5771 issue: 12 year: 2020 publication-title: J. Chem. Inf. Model. – volume: 78 start-page: 162 issue: 1 year: 2010 publication-title: Proteins – volume: 93 start-page: 2395 issue: 7 year: 1993 publication-title: Chem. Rev. – volume: 7 start-page: 1 year: 1979 publication-title: Ann. Stat. – volume: 44 start-page: 1202 issue: 1 year: 2016 publication-title: Nucleic Acids Res. – volume: 294 start-page: 157 year: 2019 publication-title: J. Biol. Chem. – volume: 51 start-page: 12 issue: 2 year: 2019 publication-title: Exp. Mol. Med. – volume: 30 start-page: 2785 issue: 16 year: 2009 publication-title: J. Comput. Chem. – volume: 10 start-page: 991 issue: 53 year: 2020 publication-title: RSC Adv. – volume: 61 start-page: 2302 issue: 5 year: 2021 publication-title: J. Chem. Inf. Model. – volume: 61 start-page: 3891 year: 2021 publication-title: J. Chem. Inf. Model. – volume: 36 start-page: 3219 issue: 22 year: 1980 publication-title: Tetrahedron – volume: 11 start-page: 2926 year: 2021 publication-title: RSC Adv. – volume: 41 start-page: 611 issue: 7 year: 2020 publication-title: J. Comput. Chem. – volume: 14 start-page: 501 issue: 6 year: 1995 publication-title: Quant. Struct.‐Act. Rel. – volume: 4 issue: 11 year: 2020 publication-title: J. Phys. Commun. – volume: 676 start-page: 12 year: 2017 publication-title: Chem. Phys. Lett. – volume: 11 start-page: 5065 issue: 9 year: 2021 publication-title: RSC Adv. – volume: 9 start-page: 833 issue: 43 year: 2019 publication-title: RSC Adv. – volume: 19 start-page: 19 year: 1978 publication-title: Tetrahedron Lett. – volume: 16 start-page: 7160 issue: 11 year: 2020 publication-title: J. Chem. Theor. Comput. – volume: 29 start-page: 298 issue: 1 year: 2020 publication-title: Protein Sci. – volume: 11 start-page: 478 issue: 28 year: 2021 publication-title: RSC Adv. – volume: 39 start-page: 621 issue: 11 year: 2018 publication-title: J. Comput. Chem. – volume: 47 start-page: 930 issue: 1 year: 2018 publication-title: Nucleic Acids Res. – volume: 10 start-page: 284 year: 2020 publication-title: RSC Adv. – volume: 11 start-page: 71 year: 2014 publication-title: Nat. Chem. Biol. – volume: 31 start-page: 455 year: 2010 publication-title: J. Comput. Chem. – start-page: 85 year: 2017 – volume: 14 start-page: 157 issue: 6 year: 2013 publication-title: Int. J. Mol. Sci. – volume: 98 start-page: 937 issue: 26 year: 2001 publication-title: Proc. Natl. Acad. Sci. U. S. A. – volume: 54 start-page: 2309 issue: 8 year: 2014 publication-title: J. Chem. Inf. Model. – volume: 122 start-page: 9435 issue: 41 year: 2018 publication-title: J. Phys. Chem. B – volume: 11 start-page: 905 year: 2016 publication-title: Nat. Protoc. – volume: 91 start-page: 91 year: 2019 publication-title: J. Mol. Graph. Model. – volume: 148 issue: 10 year: 2018 publication-title: J. Chem. Phys. – volume: 3 start-page: 300 issue: 5 year: 1935 publication-title: J. Chem. Phys. – volume: 10 start-page: 986 issue: 1 year: 2020 publication-title: Sci. Rep. – volume: 120 start-page: 2788 year: 2020 publication-title: Chem. Rev. – volume: 33 start-page: 889 issue: 12 year: 2000 publication-title: Acc. Chem. Res. – volume: 120 start-page: 2710 issue: 12 year: 1998 publication-title: J. Am. Chem. Soc. – volume: 10 issue: 4 year: 2020 publication-title: Wiley Interdiscip. Rev. Comput. Mol. Sci. – volume: 60 start-page: 204 issue: 1 year: 2020 publication-title: J. Chem. Inf. Model. – volume: 18 start-page: 964 issue: 18 year: 2016 publication-title: Phys. Chem. Chem. Phys. – volume: 10 start-page: 824 issue: 1 year: 2019 publication-title: Nat. Commun. – volume: 130 issue: 16 year: 2009 publication-title: J. Chem. Phys. – volume: 22 start-page: 1420 issue: 8 year: 1954 publication-title: J. Chem. Phys. – volume: 21 start-page: 175 issue: 1 year: 2017 publication-title: Mol. Divers. – volume: 47 start-page: 1739 issue: 7 year: 2004 publication-title: J. Med. Chem. – volume: 58 start-page: 1697 issue: 8 year: 2018 publication-title: J. Chem. Inf. Model. – volume: 267 start-page: 727 issue: 3 year: 1997 publication-title: J. Mol. Biol. – volume: 12 start-page: 27 issue: 1 year: 1998 publication-title: J. Comput. Aid. Mol. Des. – start-page: 305 year: 2012 – volume: 43 start-page: 3786 issue: 20 year: 2000 publication-title: J. Med. Chem. – volume: 4 start-page: 3887 issue: 2 year: 2019 publication-title: ACS Omega – volume: 116 start-page: 5520 issue: 9 year: 2016 publication-title: Chem. Rev. – volume: 10 start-page: 7732 issue: 13 year: 2020 publication-title: RSC Adv. – volume: 10 start-page: 284 year: 2020 ident: e_1_2_6_40_1 publication-title: RSC Adv. – ident: e_1_2_6_57_1 doi: 10.1038/s41467-019-08646-8 – ident: e_1_2_6_60_1 doi: 10.1016/S0040-4039(01)94977-9 – ident: e_1_2_6_62_1 doi: 10.1016/j.softx.2015.06.001 – volume: 47 start-page: 930 issue: 1 year: 2018 ident: e_1_2_6_47_1 publication-title: Nucleic Acids Res. – ident: e_1_2_6_42_1 doi: 10.1039/D1RA02529E – ident: e_1_2_6_48_1 doi: 10.1093/nar/gkv951 – ident: e_1_2_6_65_1 doi: 10.1039/D0RA10529E – ident: e_1_2_6_21_1 doi: 10.1063/1.5017136 – ident: e_1_2_6_27_1 doi: 10.1021/ar000033j – ident: e_1_2_6_5_1 doi: 10.1021/acs.jctc.0c00634 – ident: e_1_2_6_53_1 doi: 10.1038/nprot.2016.051 – ident: e_1_2_6_50_1 doi: 10.1002/jcc.21334 – ident: e_1_2_6_44_1 doi: 10.1016/j.jmgm.2019.06.008 – ident: e_1_2_6_14_1 doi: 10.1021/jm030644s – ident: e_1_2_6_11_1 doi: 10.1006/jmbi.1996.0897 – ident: e_1_2_6_26_1 doi: 10.1039/C9RA09583G – ident: e_1_2_6_64_1 doi: 10.1039/D0RA09858B – ident: e_1_2_6_2_1 doi: 10.1021/acs.chemrev.5b00630 – ident: e_1_2_6_56_1 doi: 10.1074/jbc.RA119.009509 – ident: e_1_2_6_32_1 doi: 10.1063/1.1749657 – ident: e_1_2_6_39_1 doi: 10.1021/acs.jpcb.8b03277 – ident: e_1_2_6_19_1 doi: 10.1021/acsomega.8b03258 – ident: e_1_2_6_63_1 doi: 10.1214/aos/1176344552 – ident: e_1_2_6_4_1 doi: 10.1002/jcc.26439 – ident: e_1_2_6_10_1 doi: 10.1002/wcms.1455 – ident: e_1_2_6_55_1 doi: 10.1073/pnas.1805020115 – ident: e_1_2_6_12_1 doi: 10.1002/jcc.21256 – volume: 98 start-page: 937 issue: 26 year: 2001 ident: e_1_2_6_29_1 publication-title: Proc. Natl. Acad. Sci. U. S. A. – ident: e_1_2_6_24_1 doi: 10.1007/978-1-61779-465-0_20 – ident: e_1_2_6_45_1 doi: 10.1007/s11030-016-9717-4 – ident: e_1_2_6_59_1 doi: 10.1021/acs.jcim.1c00203 – ident: e_1_2_6_30_1 doi: 10.1063/1.1740409 – ident: e_1_2_6_22_1 doi: 10.1088/2399-6528/abcbac – ident: e_1_2_6_46_1 doi: 10.1021/acs.jcim.5b00559 – ident: e_1_2_6_15_1 doi: 10.1002/qsar.19950140602 – ident: e_1_2_6_35_1 doi: 10.1063/1.3119261 – volume: 18 start-page: 964 issue: 18 year: 2016 ident: e_1_2_6_68_1 publication-title: Phys. Chem. Chem. Phys. – ident: e_1_2_6_8_1 doi: 10.1146/annurev.pa.27.040187.001205 – ident: e_1_2_6_66_1 doi: 10.1038/s41598-020-57781-6 – ident: e_1_2_6_34_1 doi: 10.1103/PhysRevE.56.5018 – ident: e_1_2_6_37_1 doi: 10.1021/ct1001768 – ident: e_1_2_6_38_1 doi: 10.3390/ijms140612157 – ident: e_1_2_6_67_1 doi: 10.1021/acs.jcim.1c00159 – ident: e_1_2_6_25_1 doi: 10.1021/ci500301s – start-page: 85 volume-title: Antibiotics: Methods and Protocols year: 2017 ident: e_1_2_6_6_1 doi: 10.1007/978-1-4939-6634-9_5 – ident: e_1_2_6_23_1 doi: 10.1023/A:1007930623000 – ident: e_1_2_6_49_1 doi: 10.1124/pr.112.007336 – ident: e_1_2_6_54_1 doi: 10.1038/nchembio.1697 – ident: e_1_2_6_16_1 doi: 10.1002/prot.22543 – ident: e_1_2_6_3_1 doi: 10.1038/s12276-019-0205-7 – ident: e_1_2_6_31_1 doi: 10.1021/ja9738539 – ident: e_1_2_6_18_1 doi: 10.1016/j.cplett.2017.03.034 – ident: e_1_2_6_13_1 doi: 10.1021/jm0306430 – ident: e_1_2_6_17_1 doi: 10.1002/jcc.24502 – ident: e_1_2_6_61_1 doi: 10.1016/0040-4020(80)80168-2 – volume: 10 start-page: 991 issue: 53 year: 2020 ident: e_1_2_6_43_1 publication-title: RSC Adv. – ident: e_1_2_6_33_1 doi: 10.1021/cr00023a004 – ident: e_1_2_6_9_1 doi: 10.1021/acs.chemrev.0c00534 – ident: e_1_2_6_20_1 doi: 10.1016/j.jmgm.2019.107441 – ident: e_1_2_6_51_1 doi: 10.1021/acs.jcim.8b00312 – ident: e_1_2_6_52_1 doi: 10.1021/acs.jcim.9b00778 – ident: e_1_2_6_7_1 doi: 10.1002/jcc.25139 – ident: e_1_2_6_69_1 doi: 10.1002/pro.3784 – ident: e_1_2_6_36_1 doi: 10.1002/jcc.26130 – ident: e_1_2_6_28_1 doi: 10.1021/jm000241h – ident: e_1_2_6_41_1 doi: 10.1021/acs.jcim.0c00491 – ident: e_1_2_6_58_1 doi: 10.1039/C9RA01981B
SSID	ssj0003564
Score	2.5712388
Snippet	AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to... AutoDock Vina (Vina) achieved a very high docking‐success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to... AutoDock Vina (Vina) achieved a very high docking-success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to... AutoDock Vina (Vina) achieved a very high docking-success rate, p^ , but give a rather low correlation coefficient, R , for binding affinity with respect to...
SourceID	proquest pubmed crossref wiley
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	160
SubjectTerms	Affinity AutoDock Vina Binding binding affinity Correlation coefficients Docking empirical parameter Hydrogen bonds Ligands Mathematical analysis modified Vina Parameters Ranking screening
Title	Improving ligand‐ranking of AutoDock Vina by changing the empirical parameters
URI	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fjcc.26779 https://www.ncbi.nlm.nih.gov/pubmed/34716930 https://www.proquest.com/docview/2607094914 https://www.proquest.com/docview/2590136543
Volume	43
WOSCitedRecordID	wos000712877900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1096-987X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0003564 issn: 0192-8651 databaseCode: DRFUL dateStart: 19960101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS9xAEB_0LNSXVvvlVSvb0oe-pCbZ3csGn-Tao4iIiMq9hf2KXKs58e4Kvvkn-Df6l3Rm81GkLQh9C5kJG2Z2dn5MMr8B-KgRBiQSPeAwm0VCWBUpZ9PIWee0x4xahmEwZwfZ4aEaj_OjJdhte2Fqfoiu4EaREc5rCnBtZju_SUO_W_s5HWRZvgwrKe5b2YOVL8ej04PuIOayZo9CEBOpgUxaYqE43ekefpiO_sCYDyFryDmj5__1tmvwrIGabK_eG-uw5KsX8HTYTnh7CUddRYFdTM515e5v72iGO92YlmxvMZ9iDvrBziaVZuaGhSZhEiJoZP7yahLoRRixh1_SXzWzV3A6-noy_BY1ExYiyyXPI6VL7f0A41AoLYxztlTcWeWoLqQ1cWEZ4eIyjP32A5UIRHde-ST3JU234a-hV00rvwHMSSmNKNNYGy20SY2RmS2dkYnRHCV9-NQaurAN_ThNwbgoauLktEATFcFEffjQqV7VnBt_U9pqvVU0YTdDCZ5gucgTXO59J0ar0lcQXfnpAnWo25ZTS20f3tRe7lbhgsiDeIwvG5z57-WL_eEwXLx9vOomrKbUPBFTxW8LevPrhX8HT-zP-WR2vQ3L2VhtN3v4F_HK87c
linkProvider	Wiley-Blackwell
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LTxRBEK4gmMAF8ckiaGs8eBmZme7e7Um8kJUN6rohBgi3Sb_GLI9Zwu6ScOMn8Bv9JVb1PAwRExNvk6madKceXZXqqa8A3mlMAxKJGnAYzSIhrIqUs2nkrHPaY0QtwjCYo2FvNFLHx9n-AnxsemEqfIi24EaeEc5rcnAqSG__Rg09sfZD2u31sgewJNCM0L6XPn0fHA7bk5jLCj4Ks5hIdWXSIAvF6Xb78d149EeSeTdnDUFn8Oj_trsGq3WyyXYq63gMC758Asv9ZsbbU9hvawrsbPxDl-7nzS1NcacXk4LtzGcTjEKn7GhcamauWWgTJiKmjcyfX4wDwAgj_PBz-q9m-gwOB7sH_b2onrEQWS55FildaO-76IlCaWGcs4XizipHlSGtCQ3LCBcXYfC376pEYH7nlU8yX9B8G_4cFstJ6deBOSmlEUUaa6OFNqkxsmcLZ2RiNEdKB943ks5tDUBOczDO8go6Oc1RRHkQUQfetqwXFerGfUybjbry2vGmSMEzLBNZgsu9ackoVboH0aWfzJGH-m05NdV24EWl5nYVLgg-iMe42aDNvy-ff-n3w8PGv7O-huW9g2_DfPh59PUlrKTUShFT_W8TFmeXc78FD-3VbDy9fFWb8i_Jbva_
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9NAEB6VFFEuvB-BAgviwMXU9u4ma4lLlRLxiKII0ao3a58opXWiJkHixk_gN_JLmFk_UAVISNwsz1hrzezsjMae7wN4rrEMyCR6wGE2S4SwKlHO5omzzmmPGTVEMpijyXA6VcfHxWwLXrWzMDU-RNdwo8iI5zUFuF-6sPcLNfTE2pf5YDgsLsG2IBKZHmwffBgfTrqTmMsaPgqrmEQNZNYiC6X5XvfwxXz0W5F5sWaNSWd8_f9e9wZca4pNtl_vjpuw5atbsDNqOd5uw6zrKbDT-SdduR_fvhOLO91YBLa_WS8wC31mR_NKM_OVxTFhEmLZyPzZch4BRhjhh5_RfzWrO3A4fv1x9CZpOBYSyyUvEqWD9n6AkSiUFsY5GxR3VjnqDGlNaFhGuDRE4m8_UJnA-s4rnxU-EL8Nvwu9alH5-8CclNKIkKfaaKFNbowc2uCMzIzmKOnDi9bSpW0AyIkH47SsoZPzEk1URhP14VmnuqxRN_6ktNu6q2wCb4USPMMKUWS43NNOjFal7yC68osN6tC8Laeh2j7cq93crcIFwQfxFF82evPvy5fvRqN48eDfVZ_AldnBuJy8nb5_CFdzmqRIqf23C731-cY_gsv2y3q-On_c7OSfYqf2Og
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Improving+ligand%E2%80%90ranking+of+AutoDock+Vina+by+changing+the+empirical+parameters&rft.jtitle=Journal+of+computational+chemistry&rft.au=Pham%2C+T.+Ngoc+Han&rft.au=Nguyen%2C+Trung+Hai&rft.au=Tam%2C+Nguyen+Minh&rft.au=Y.+Vu%2C+Thien&rft.date=2022-01-30&rft.issn=0192-8651&rft.eissn=1096-987X&rft.volume=43&rft.issue=3&rft.spage=160&rft.epage=169&rft_id=info:doi/10.1002%2Fjcc.26779&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_jcc_26779
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0192-8651&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0192-8651&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0192-8651&client=summon