Chemistry Reference
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[334] Hristovski D, Daeroski S, Peterlin B, Roi-Hristovski A. Supporting discovery in medicine
by association rule mining of bibliographic databases. Stud. Health Technol. Inform., 2001,
84: 1344-1348.
[335] Ghose AK, Herbertz T, Hudkins RL, Dorsey BD, Mallamo JP. Knowledge-based, central
nervous system (CNS) lead selection and lead optimization for CNS drug discovery. ACS
Chem. neurosci., 2012, 3: 50-68.
[336] Ghose AK, Herbertz T, Pippin DA, Salvino JM, Mallamo JP. Knowledge based prediction
of ligand binding modes and rational inhibitor design for kinase drug discovery. J. Med.
Chem., 2008, 51: 5149-5171.
[337] Simmons K, Kinney J, Owens A, Kleier DA, Bloch K, Argentar D, Walsh A, Vaidyanathan
G. Practical outcomes of applying ensemble machine learning classifiers to High-
Throughput Screening (HTS) data analysis and screening. J. Chem. Inf. Model., 2008, 48:
2196-2206.
[338] Blundell TL, Sibanda BL, Sternberg MJ, Thornton JM. Knowledge-based prediction of
protein structures and the design of novel molecules. Nature, 1987, 326:347-352
[339] Parenti MD, Rastelli G, Advances and applications of binding affinity prediction methods
in drug discovery, Biotechnology Advances, 2012, 30: 244-250.
[340] de Azevedo WF, Dias R. Computational methods for calculation of ligand-binding affinity.
Curr. Drug Targets. 2008, 9: 1031-1039.
[341] Jorgensen WJ. Efficient drug lead discovery and optimization. Acc. Chem. Res. 2009, 42:
724-733.
[342] Yuriev E and Ramsland PA, Latest developments in molecular docking : 2010-2011 in
review. J. Mol. Recognition, 2013, 26:215-239.
[343] Wang W, He W, Zhou X and Chen X. Optimization of molecular docking scores with
support vector rank regression. Proteins. 2013, 81:1386-1398.
[344] Zheng Z and Merz Jr. KM. Development of the knowledge-based and empirical combined
scoring algorithm (KECSA) to score protein-ligand interactions. Chem. Inf. And modeling.
2013, 53:1073-1083.
[345] Fan H, Schneidman-Duhovny DS, Irwin JJ, Dong G, Shoichet BK and Sali A, Statistical
potential for modeling and ranking of protein-ligand interactions. Chemical Information
and Modeling, 2011,51:3078-3092.
[346] Durant JD, Friedman AJ, Rogers KE and McCammon JA. Comparing neural-network scoring functions and the state
of the art: Applications to common library screening. Chemical Informatin and modeling, J. Chem. Informaiton and
modeling, 2013,53:726-1735.
[347] Liu J, He X and Zhang JZH. Improving the scoring of protein-ligand binding affinity by
including the effects of structural water and electronic polarization. Chemical information
and modeling, 2013,53:1306-1314.
[348] Sastry GM, Inakolly VSS and Sherman W, Boosting virtual screening enrichments with
data fusion:Coalescing hits from two-dimensional fingerprints, shape and docking. J.
Chem. Information and Modeling. 2013, 53:1531-1542.
[349] Korb O, McCabe P, Cole J. The ensemble performance index: An improved measure for
assessing ensemble pose prediction performance. J. Chem. Information and Modeling,
2011, 51:2915-2919.
[350] Guimaraes CRW. A direct comparison of the MM-GB/SA scoring procedure and free-
energy perturbation calculations using carbonic anhydrase as a test case: strengths and
pitfalls of each compound, J. of Chemical Theory and Computation, 2011,7:2296-2306.
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