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Items: 1 to 20 of 109

1.

Effects of inductive bias on computational evaluations of ligand-based modeling and on drug discovery.

Cleves AE, Jain AN.

J Comput Aided Mol Des. 2008 Mar-Apr;22(3-4):147-59.

PMID:
18074107
2.

Bias, reporting, and sharing: computational evaluations of docking methods.

Jain AN.

J Comput Aided Mol Des. 2008 Mar-Apr;22(3-4):201-12.

PMID:
18075713
3.
4.

Consensus scoring for protein-ligand interactions.

Feher M.

Drug Discov Today. 2006 May;11(9-10):421-8. Review.

PMID:
16635804
5.

Generalized modeling of enzyme-ligand interactions using proteochemometrics and local protein substructures.

Strömbergsson H, Kryshtafovych A, Prusis P, Fidelis K, Wikberg JE, Komorowski J, Hvidsten TR.

Proteins. 2006 Nov 15;65(3):568-79.

PMID:
16948162
6.

Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration.

Lo YT, Wang HW, Pai TW, Tzou WS, Hsu HH, Chang HT.

BMC Bioinformatics. 2013;14 Suppl 4:S4. doi: 10.1186/1471-2105-14-S4-S4.

7.

Prediction of protein-ligand binding affinities using multiple instance learning.

Teramoto R, Kashima H.

J Mol Graph Model. 2010 Nov;29(3):492-7. doi: 10.1016/j.jmgm.2010.09.006.

PMID:
20965757
8.
9.

A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction.

Hoffmann B, Zaslavskiy M, Vert JP, Stoven V.

BMC Bioinformatics. 2010 Feb 22;11:99. doi: 10.1186/1471-2105-11-99.

10.

Impact of computational structure-based methods on drug discovery.

Reynolds CH.

Curr Pharm Des. 2014;20(20):3380-6. Review.

PMID:
23947642
11.

Ligand mapping on protein surfaces by the 3D-RISM theory: toward computational fragment-based drug design.

Imai T, Oda K, Kovalenko A, Hirata F, Kidera A.

J Am Chem Soc. 2009 Sep 2;131(34):12430-40. doi: 10.1021/ja905029t.

PMID:
19655800
12.

Enhancing drug discovery through in silico screening: strategies to increase true positives retrieval rates.

Kirchmair J, Distinto S, Schuster D, Spitzer G, Langer T, Wolber G.

Curr Med Chem. 2008;15(20):2040-53. Review.

PMID:
18691055
13.

Molecular modeling of hydration in drug design.

Mancera RL.

Curr Opin Drug Discov Devel. 2007 May;10(3):275-80. Review.

PMID:
17554853
14.

Computational chemogenomics: is it more than inductive transfer?

Brown JB, Okuno Y, Marcou G, Varnek A, Horvath D.

J Comput Aided Mol Des. 2014 Jun;28(6):597-618. doi: 10.1007/s10822-014-9743-1.

PMID:
24771144
15.

Computational approaches for ligand discovery and design in class-A G protein- coupled receptors.

Rodríguez D, Gutiérrez-de-Terán H.

Curr Pharm Des. 2013;19(12):2216-36. Review.

PMID:
23016842
16.

A novel approach to local similarity of protein binding sites substantially improves computational drug design results.

Ramensky V, Sobol A, Zaitseva N, Rubinov A, Zosimov V.

Proteins. 2007 Nov 1;69(2):349-57.

PMID:
17623865
17.

Advances in the prediction of protein-peptide binding affinities: implications for peptide-based drug discovery.

Audie J, Swanson J.

Chem Biol Drug Des. 2013 Jan;81(1):50-60. doi: 10.1111/cbdd.12076. Review.

PMID:
23066895
18.

Theoretical and computational approaches to ligand-based drug discovery.

Favia AD.

Front Biosci (Landmark Ed). 2011 Jan 1;16:1276-90. Review.

PMID:
21196231
19.

Knowledge-based scoring functions in drug design: 3. A two-dimensional knowledge-based hydrogen-bonding potential for the prediction of protein-ligand interactions.

Zheng M, Xiong B, Luo C, Li S, Liu X, Shen Q, Li J, Zhu W, Luo X, Jiang H.

J Chem Inf Model. 2011 Nov 28;51(11):2994-3004. doi: 10.1021/ci2003939.

PMID:
21999432
20.

Assessment of ligand-binding residue predictions in CASP9.

Schmidt T, Haas J, Gallo Cassarino T, Schwede T.

Proteins. 2011;79 Suppl 10:126-36. doi: 10.1002/prot.23174.

PMID:
21987472
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