Chemistry Reference
In-Depth Information
[318] Gunasekaran K, Ma B, Nussinov R. Is allostery an intrinsic property of all dynamic
proteins? Proteins: Struct., Funct., Bioinform., 2004, 57: 433-443.
[319] Xu Y, Colletier JP, Jiang H, Silman I, Sussman JL, Weik M. Induced-fit or preexisting
equilibrium dynamics? Lessons from protein crystallography and MD simulations on
acetylcholinesterase and implications for structure-based drug design. Protein Sci., 2008,
17: 601-605.
[320] Hammes GG, Chang YC, Oas TG. Conformational selection or induced fit: a flux
description of reaction mechanism. Proc. Natl. Acad. Sci. USA, 2009, 106: 13737-13741.
]321] Peng S, Lin X, Cho Z, Huang N, Identifying multiple-target ligands
via
computational
chemogenomics approaches, Current Topics in medicinal chemistry, 2012,12:1363-1375.
[322] Ma XH, Shi Z, Tan C, Jiang Y, Go ML, Low BC, Chen YZ, In-silico approaches to multi-
target drug discovery:computer aided multi-target drug design, multi-target virtual
screening, Pharm. Res, 2010, 27: 739-749.
[323] Dik-Lung Ma DL Chana DSH and Leung CH, Drug repositioning by structure-based
virtual screening, Chem. Soc. Rev., 2013, 42: 2130—2141.
[324] Ghemtio L, Pérez-Nueno VI, Leroux V, Asses Y, Souchet M, Mavridis L, Maigret B and
W. Ritchie DW, RDeng, Z.; Chuaqui, C.; Singh, J. Recent trends and applications in 3d
virtual screening, Combinatorial Chemistry and high-throughput screening, 2012:15:749-
769.
[325] Deng Z, Chuaqui C, Singh J. Knowledge-based design of target focused libraries using
protein-ligand interaction constraints. J. Med. Chem., 2006, 49: 490-500.
[326] Li L, Wang B, Meroueh SO. Support vector regression scoring of receptor-ligand
complexes for rank-ordering and virtual screening of chemical libraries. J. Chem. Inf.
Model., 2011, 51: 2132-2138.
[327] Xue MZ, Zheng MY, Xiong B, Li YL, Jiang HL Shen JK. Knowledge-Based Scoring
Functions in Drug Design. Developing a Target-Specific Method for Kinase-Ligand
Interactions. J. Chem. Inf. Model., 2010, 50: 1378-1386.
[328] Ghemtio L, Devignes MD, Smaïl-Tabbone M, Souchet M, Leroux V, Maigret B.
Comparison of three preprocessing filters efficiency in virtual screening: identification of
new putative LXRß regulators as a test case. J. Chem. Inf. Model., 2010, 50: 701-715.
[329] Seoud RA. Bio HCVKD: a bioinformatics knowledge discovery system for HCV drug
discovery-identifying proteins, ligands and active residues, in biological literature. Int. J.
Bioinform. Res. App., 2011, 7: 317-333.
[330] Medina-Aunon JA, Paradela A, Macht M, Thiele H, Corthals G, Albar JP. Protein
information and knowledge extractor: Discovering biological information from proteomics
data. Proteomics, 2010, 10: 3262-3271.
[331] Zheng MY, Xiong B, Luo C, Li S, Liu X, Shen QC, Li J, Zhu W L, Luo XM, Jiang H L.
Knowledge-based scoring functions in drug design: 3. A two-dimensional knowledge based
hydrogen-bonding potential for the prediction of protein ligand interactions. J. Chem. Inf.
Model., 2011, 51: 2994-3004.
[332] Shen QC, Xiong B, Zheng MY, Luo XM, Luo C, Liu XA, Du Y, Li J, Zhu WL, Shen J. K,
Jiang HL. Knowledgebased scoring functions in drug design: 2. Can the knowledge base be
enriched? J. Chem. Inf. Model., 2011, 51: 386-397.
[333] Ghose AK, Herbertz T, Salvino JM.; Mallamo JP. Knowledge-based chemoinformatic
approaches to drug discovery.Drug Discov. Today, 2006, 11: 1107-1114.
