Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 114

1.

How to improve docking accuracy of AutoDock4.2: a case study using different electrostatic potentials.

Hou X, Du J, Zhang J, Du L, Fang H, Li M.

J Chem Inf Model. 2013 Jan 28;53(1):188-200. doi: 10.1021/ci300417y. Epub 2013 Jan 2.

PMID:
23244516
2.

The effect of different electrostatic potentials on docking accuracy: a case study using DOCK5.4.

Tsai KC, Wang SH, Hsiao NW, Li M, Wang B.

Bioorg Med Chem Lett. 2008 Jun 15;18(12):3509-12. doi: 10.1016/j.bmcl.2008.05.026. Epub 2008 May 10.

PMID:
18502122
3.

A comparison of different electrostatic potentials on prediction accuracy in CoMFA and CoMSIA studies.

Tsai KC, Chen YC, Hsiao NW, Wang CL, Lin CL, Lee YC, Li M, Wang B.

Eur J Med Chem. 2010 Apr;45(4):1544-51. doi: 10.1016/j.ejmech.2009.12.063. Epub 2010 Jan 13.

PMID:
20110138
4.

Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock.

Bikadi Z, Hazai E.

J Cheminform. 2009 Sep 11;1:15. doi: 10.1186/1758-2946-1-15.

5.

Comparison of charge models for fixed-charge force fields: small-molecule hydration free energies in explicit solvent.

Mobley DL, Dumont E, Chodera JD, Dill KA.

J Phys Chem B. 2007 Mar 8;111(9):2242-54. Epub 2007 Feb 10. Erratum in: J Phys Chem B. 2011 Feb 10;115(5):1329-32.

PMID:
17291029
6.

The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models.

Kumar SP, Jha PC, Jasrai YT, Pandya HA.

J Biomol Struct Dyn. 2016;34(3):540-59. doi: 10.1080/07391102.2015.1044474. Epub 2015 May 21.

PMID:
25997097
7.

AutoDock4(Zn): an improved AutoDock force field for small-molecule docking to zinc metalloproteins.

Santos-Martins D, Forli S, Ramos MJ, Olson AJ.

J Chem Inf Model. 2014 Aug 25;54(8):2371-9. doi: 10.1021/ci500209e. Epub 2014 Jul 18.

8.

A semiempirical free energy force field with charge-based desolvation.

Huey R, Morris GM, Olson AJ, Goodsell DS.

J Comput Chem. 2007 Apr 30;28(6):1145-52.

PMID:
17274016
9.

Comparative analysis of various electrostatic potentials on docking precision against cyclin-dependent kinase 2 protein: a multiple docking approach.

Tripathi SK, Soundarya RN, Singh P, Singh SK.

Chem Biol Drug Des. 2015 Feb;85(2):107-18. doi: 10.1111/cbdd.12376. Epub 2014 Jul 12.

PMID:
24923208
10.

Comparison of several molecular docking programs: pose prediction and virtual screening accuracy.

Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y, Humblet C.

J Chem Inf Model. 2009 Jun;49(6):1455-74. doi: 10.1021/ci900056c.

PMID:
19476350
11.

Systematic and efficient side chain optimization for molecular docking using a cheapest-path procedure.

Schumann M, Armen RS.

J Comput Chem. 2013 May 30;34(14):1258-69. doi: 10.1002/jcc.23251. Epub 2013 Feb 19.

PMID:
23420703
12.

A python-based docking program utilizing a receptor bound ligand shape: PythDock.

Chung JY, Cho SJ, Hah JM.

Arch Pharm Res. 2011 Sep;34(9):1451-8. doi: 10.1007/s12272-011-0906-5. Epub 2011 Oct 6.

PMID:
21975806
13.

A new force field (ECEPP-05) for peptides, proteins, and organic molecules.

Arnautova YA, Jagielska A, Scheraga HA.

J Phys Chem B. 2006 Mar 16;110(10):5025-44.

PMID:
16526746
14.

Structure-based virtual screening for novel ligands.

Pitt WR, Calmiano MD, Kroeplien B, Taylor RD, Turner JP, King MA.

Methods Mol Biol. 2013;1008:501-19. doi: 10.1007/978-1-62703-398-5_19.

PMID:
23729265
15.

Accurate conformation-dependent molecular electrostatic potentials for high-throughput in silico drug discovery.

Puranen JS, Vainio MJ, Johnson MS.

J Comput Chem. 2010 Jun;31(8):1722-32. doi: 10.1002/jcc.21460.

PMID:
20020481
16.

Use of experimental design to optimize docking performance: the case of LiGenDock, the docking module of LiGen, a new de novo design program.

Beato C, Beccari AR, Cavazzoni C, Lorenzi S, Costantino G.

J Chem Inf Model. 2013 Jun 24;53(6):1503-17. doi: 10.1021/ci400079k. Epub 2013 Apr 30.

PMID:
23590204
17.

Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines.

Zhang X, Wong SE, Lightstone FC.

J Comput Chem. 2013 Apr 30;34(11):915-27. doi: 10.1002/jcc.23214. Epub 2013 Jan 23.

PMID:
23345155
18.

Lead finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening.

Stroganov OV, Novikov FN, Stroylov VS, Kulkov V, Chilov GG.

J Chem Inf Model. 2008 Dec;48(12):2371-85. doi: 10.1021/ci800166p.

PMID:
19007114
19.
20.

AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening.

Pencheva T, Lagorce D, Pajeva I, Villoutreix BO, Miteva MA.

BMC Bioinformatics. 2008 Oct 16;9:438. doi: 10.1186/1471-2105-9-438.

Supplemental Content

Support Center