Ligand efficiency outperforms pIC50 on both 2D MLR and 3D CoMFA models: A case study on AR antagonists

Chemical Biology and Drug Design

Volumn 86, Issue 6, 2015, Pages 1501-1517

  Li, Jiazhong ^a,b   Bai, Fang ^a   Liu, Huanxiang ^a   Gramatica, Paola ^c  

^a LANZHOU UNIVERSITY   (China)

^b Lanzhou Institute of Physics   (China)

^c UNIVERSITY OF INSUBRIA   (Italy)

Author keywords

androgen receptor antagonist; comparative molecular field analysis; ligand efficiency; multiple linear regression; surface efficiency index

Indexed keywords

ANDROGEN RECEPTOR ANTAGONIST; LIGAND;

CHEMICAL DATABASE; CHEMICAL STRUCTURE; CHEMISTRY; DRUG DESIGN; MOLECULAR DOCKING; MOLECULAR MODEL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STATISTICAL MODEL; SUPPORT VECTOR MACHINE;

ANDROGEN RECEPTOR ANTAGONISTS; DATABASES, CHEMICAL; DRUG DESIGN; LIGANDS; LINEAR MODELS; MODELS, MOLECULAR; MOLECULAR DOCKING SIMULATION; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SUPPORT VECTOR MACHINE;

EID: 84956584689     PISSN: 17470277     EISSN: 17470285     Source Type: Journal    
DOI: 10.1111/cbdd.12619     Document Type: Article

References (33)

1. Tanaka D., Tsuda Y., Shiyama T., Nishimura T., Chiyo N., Tominaga Y., Sawada N., Mimoto T., Kusunose N., (2011) A practical use of ligand efficiency indices out of the fragment-based approach: ligand efficiency-guided lead identification of soluble epoxide hydrolase inhibitors. J Med Chem; 54: 851-857.
2. Andrews P.R., Craik D.J., Martin J.L., (1984) Functional group contributions to drug-receptor interactions. J Med Chem; 27: 1648-1657.
3. Kuntz I.D., Chen K., Sharp K.A., Kollman P.A., (1999) The maximal affinity of ligands. Proc Natl Acad Sci USA; 96: 9997-10002.
4. Hopkins A.L., Groom C.R., Alex A., (2004) Ligand efficiency: a useful metric for lead selection. Drug Discov Today; 9: 430-431.
5. Abad-Zapatero C., Metz J.T., (2005) Ligand efficiency indices as guideposts for drug discovery. Drug Discov Today; 10: 464-469.
6. García-Sosa A.T., Hetényi C., Maran U., (2010) Drug efficiency indices for improvement of molecular docking scoring functions. J Comput Chem; 31: 174-184.
7. Sugaya N., (2014) Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins. J Chem Inf Model; 54: 2751-2763.
8. Sugaya N., (2013) Training based on ligand efficiency improves prediction of bioactivities of ligands and drug target proteins in a machine learning approach. J Chem Inf Model; 53: 2525-2537.
9. Lee S.Y., Song C.H., Xie Y.B., Jung C., Choi H.S., Lee K., (2014) SMILE upregulated by metformin inhibits the function of androgen receptor in prostate cancer cells. Cancer Lett; 354: 390-397.
10. Wildschut H.I., Monincx W., (1994) Vasectomy and the risk of prostate cancer. Bull World Health Organ; 72: 777-778.
11. Siegel R., Ma J., Zou Z., Jemal A., (2014) A cancer journal for clinicians. CA Cancer J Clin; 64: 9-29.
12. WHO (2014) Cancer country profiles 2014. WHO. http://www.who.int/cancer/country-profiles/usa-en.pdf
13. Siegel R., Ward E., Brawley O., Jemal A., (2011) Cancer statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer J Clin; 61: 212-236.
14. Sim H.G., Cheng C.W., (2005) Changing demography of prostate cancer in Asia. Eur J Cancer; 41: 834-845.
15. Nirschl A.A., Zou Y., Krystek S.R. Jr, Sutton J.C., Simpkins L.M., Lupisella J.A., Kuhns J.E., et al,. (2009) N-Aryl-oxazolidin-2-imine muscle selective androgen receptor modulators enhance potency through pharmacophore reorientation. J Med Chem; 52: 2794-2798.
16. Balog A., Salvati M.E., Shan W., Mathur A., Leith L.W., Wei D.D., Attar R.M., Geng J., Rizzo C.A., Wang C., Krystek S.R., Tokarski J.S., Hunt J.T., Gottardis M., Weinmann R., (2004) The synthesis and evaluation of [2.2.1]-bicycloazahydantoins as androgen receptor antagonists. Bioorg Med Chem Lett; 14: 6107-6111.
17. Salvati M.E., Balog A., Shan W., Wei D.D., Pickering D., Attar R.M., Geng J., Rizzo C.A., Gottardis M.M., Weinmann R., Krystek S.R., Sack J., An Y., Kish K., (2005) Structure based approach to the design of bicyclic-1H-isoindole-1,3(2H)-dione based androgen receptor antagonists. Bioorg Med Chem Lett; 15: 271-276.
18. Salvati M.E., Balog A., Wei D.D., Pickering D., Attar R.M., Geng J., Rizzo C.A., Hunt J.T., Gottardis M.M., Weinmann R., Martinez R., (2005) Identification of a novel class of androgen receptor antagonists based on the bicyclic-1H-isoindole-1,3(2H)-dione nucleus. Bioorg Med Chem Lett; 15: 389-393.
19. Xiao H.Y., Balog A., Attar R.M., Fairfax D., Fleming L.B., Holst C.L., Martin G.S., et al,. (2010) Design and synthesis of 4-[3,5-dioxo-11-oxa-4,9-diazatricyclo[5.3.1.0(2,6)]undec-4-yl]-2-trifluoromethyl-benzonitriles as androgen receptor antagonists. Bioorg Med Chem Lett; 20: 4491-4495.
20. Abad-Zapatero C., (2007) Ligand efficiency indices for effective drug discovery. Exp Opin Drug Discov; 2: 469-488.
21. Gramatica P., Chirico N., Papa E., Cassani S., Kovarich S., (2013) QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem; 34: 2121-2132.
22. Gramatica P., Cassani S., Chirico N., (2014) QSARINS-Chem: insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J Comput Chem; 35: 1036-1044.
23. Yap C.W., (2011) PaDEL-Descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem; 32: 1466-1474.
24. Cramer R.D., Patterson D.E., Bunce J.D., (1998) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc; 110: 5959-5967.
25. Pourbasheer E., Aalizadeh R., Shokouhi Tabar S., Ganjali M.R., Norouzi P., Shadmanesh J., (2014) 2D and 3D quantitative structure-activity relationship study of hepatitis C virus NS5B polymerase inhibitors by comparative molecular field analysis and comparative molecular similarity indices analysis methods. J Chem Inf Model; 54: 2902-2914.
26. Shi L.M., Fang H., Tong W., Wu J., Perkins R., Blair R.M., Branham W.S., Dial S.L., Moland C.L., Sheehan D.M., (2001) QSAR models using a large diverse set of estrogens. J Chem Inf Comput Sci; 41: 186-195.
27. Schüürmann G., Ebert R.U., Chen J., Wang B., Kühne R., (2008) External validation and prediction employing the predictive squared correlation coefficient test set activity mean versus training set activity mean. J Chem Inf Model; 48: 2140-2145.
28. 2 parameter for QSAR validation. J Chem Inf Model; 49: 1669-1678.
29. Lin L.I., (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics; 45: 255-268.
30. Chirico N., Gramatica P., (2011) Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model; 51: 2320-2335.
31. Chirico N., Gramatica P., (2012) Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model; 52: 2044-2058.
32. Orita M., Ohno K., Niimi T., (2009) Two 'Golden Ratio' indices in fragment-based drug discovery. Drug Discov Today; 14: 321-328.
33. Reitz A.B., Smith G.R., Tounge B.A., Reynolds C.H., (2009) Hit triage using efficiency indices after screening of compound libraries in drug discovery. Curr Top Med Chem; 9: 1718-1724.