In silico evaluation of logD7.4 and comparison with other prediction methods

Journal of Chemometrics

Volumn 29, Issue 7, 2015, Pages 389-398

  Wang, Jian Bing ^a   Cao, Dong Sheng ^a   Zhu, Min Feng ^a   Yun, Yong Huan ^a   Xiao, Nan ^a   Liang, Yi Zeng ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Distribution coefficient; Genetic algorithm (GA); Lipophilicity; LogD7.4; Quantitative structure property relationship (QSPR); Support vector machine (SVM)

Indexed keywords

ALCOHOLS; FORECASTING; LEAST SQUARES APPROXIMATIONS; MEAN SQUARE ERROR; SUPPORT VECTOR MACHINES;

DISTRIBUTION COEFFICIENT; GENETIC ALGORITHM; LIPOPHILICITY; LOGD7.4; N-OCTANOL; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS; QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIP; SUPPORT VECTOR MACHINE; SUPPORT VECTOR MACHINE MODELS; SUPPORT VECTORS MACHINE;

GENETIC ALGORITHMS;

EID: 84935753513     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.2718     Document Type: Article

References (65)

1. Renau TE, Sanchez JP, Shapiro MA, Dever JA, Gracheck SJ, Domagala JM. Effect of lipophilicity at N-1 on activity of fluoroquinolones against mycobacteria. J. Med. Chem. 1995; 38: 2974-2977.
2. AL-Saadi D, Sneader W, Waton N. Relationships between lipophilic character and biological activity of new potential long-acting local anesthetics. Med. J. Islam. Repub. Iran 1996; 10: 53-57.
3. Wils P, Warnery A, Phung-Ba V, Legrain S, Scherman D. High lipophilicity decreases drug transport across intestinal epithelial cells. J. Pharmacol. Exp. Therapeut. 1994; 269: 654-658.
4. Waring MJ. Lipophilicity in drug discovery. Expet Opin. Drug. Discov. 2010; 5: 235-248.
5. Remko M, Boháč A, Kováčiková L. Molecular structure, pKa, lipophilicity, solubility, absorption, polar surface area, and blood brain barrier penetration of some antiangiogenic agents. J. Struct. Chem. 2011; 22: 635-648.
6. Arnott JA, Planey SL. The influence of lipophilicity in drug discovery and design. Expet Opin. Drug. Discov. 2012; 7: 863-875.
7. Testa B, Crivori P, Reist M, Carrupt P-A. The influence of lipophilicity on the pharmacokinetic behavior of drugs: concepts and examples. Perspect. Drug Discov. Des. 2000; 19: 179-211.
8. Leo A, Hansch C, Elkins D. Partition coefficients and their uses. Chem. Rev. 1971; 71: 525-616.
9. NU SRR. Evaluation of the use of partition coefficients and molecular surface properties as predictors of drug absorption: a provisional biopharmaceutical classification of the list of national essential medicines of Pakistan. Daru 2011; 19: 83-99.
10. Souza ES, Zaramello L, Kuhnen CA, Junkes BS, Yunes RA, Heinzen VEF. Estimating the octanol/water partition coefficient for aliphatic organic compounds using semi-empirical electrotopological index. Int. J. Mol. Sci. 2011; 12: 7250-7264.
11. Cao D-S, Xu Q-S, Liang Y-Z, Chen X, Li H-D. Prediction of aqueous solubility of druglike organic compounds using partial least squares, back-propagation network and support vector machine. J. Chemometr. 2010; 24: 584-595.
12. Pollastri MP. Overview on the rule of five. Curr. Protoc. 2010; 9: 2.
13. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 2012; 64: 4-17.
14. Bhal SK, Kassam K, Peirson IG, Pearl GM. The rule of five revisited: applying log D in place of log P in drug-likeness filters. Mol. Pharm. 2007; 4: 556-560.
15. Comer J, Tam KY. Lipophilicity profiles: theory and measurement. In Pharmacokinetic Optimization in Drug Research: Biological, Physicochemical and Computational Strategies, Testa B, Waterbeemd H, Folkers G, Guy R (eds.). VHCA: Zurich, 2001; 275-304.
16. Ermondi G, Lorenti M, Caron G. Contribution of ionization and lipophilicity to drug binding to albumin: a preliminary step toward biodistribution prediction. J. Med. Chem. 2004; 47: 3949-3961.
17. Zhivkova Z, Doytchinova I. Quantitative structure-clearance relationships of acidic drugs. Mol. Pharm. 2013; 10: 3758-3768.
18. Kah M, Brown CD. Log D: lipophilicity for ionisable compounds. Chemosphere 2008; 72: 1401-1408.
19. Lavine BK, Workman J. Chemometrics. Anal. Chem. 2002; 74: 2763-2770.
20. Xing L, Glen RC. Novel methods for the prediction of logP, pKa, and logD. J. Chem. Inf. Comput. Sci. 2002; 42: 796-805.
21. Tetko IV, Bruneau P. Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database. J. Pharm. Sci. 2004; 93: 3103-3110.
22. Tetko IV, Poda GI. Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds. J. Med. Chem. 2004; 47: 5601-5604.
23. Viswanadhan VN, Ghose AK, Revankar GR, Robins RK. Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J. Chem. Inf. Comput. Sci. 1989; 29: 163-172.
24. Csizmadia F, Tsantili-Kakoulidou A, Panderi I, Darvas F. Prediction of distribution coefficient from structure. 1. Estimation method. J. Pharm. Sci. 1997; 86: 865-871.
25. 7.4 modeling using Bayesian regularized neural networks. Assessment and correction of the errors of prediction. J. Chem. Inf. Model. 2006; 46: 1379-1387.
26. Krejsa CM, Horvath D, Rogalski SL, Penzotti JE, Mao B, Barbosa F, Migeon JC. Predicting ADME properties and side effects: the BioPrint approach. Curr. Opin. Drug Discov. Dev. 2003; 6: 470-480.
27. Eckert H, Bajorath J. Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov. Today 2007; 12: 225-233.
28. Esposito EX, Hopfinger AJ, Madura JD. Methods for applying the quantitative structure-activity relationship paradigm.In Chemoinformatics, Springer: New York, 2004; 131-213.
29. Kennard RW, Stone LA. Computer aided design of experiments. Technometrics 1969; 11: 137-148.
30. Kocjančič R, Zupan J. Modelling of the river flowrate: the influence of the training set selection. Chemometr. Intell. Lab. Syst. 2000; 54: 21-34.
31. Vapnik V. The Nature of Statistical Learning Theory, Springer: New York, 2000.
32. Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines, Cambridge University Press Cambridge: United Kingdom, 2000.
33. Scholkopf B, Smola A. Learning with Kernels, MIT press Cambridge: Cambridge, 2002.
34. Liang YZ, Xu QS, Li H-D, Cao DS. Support Vector Machines and Their Application in Chemistry and Biotechnology, CRC Press: New York, 2011.
35. Vapnik V. Statistical Learning Theory, Wiley: New York, 1998.
36. Cao DS, Liang YZ, Xu QS, Hu QN, Zhang LX, Fu GH. Exploring nonlinear relationship in chemical data using kernel-based methods. Chemometr. Intell. Lab. Syst. 2011; 107: 106-115.
37. Li H, Yap C, Ung C, Xue Y, Li Z, Han L, Lin H, Chen YZ. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J. Pharm. Sci. 2007; 96: 2838-2860.
38. Caballero J, Fernández L, Garriga M, Abreu JI, Collina S, Fernández M. Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines. J. Mol. Graph. Model. 2007; 26: 166-178.
39. Zhao C, Zhang H, Zhang X, Liu M, Hu Z, Fan B. Application of support vector machine (SVM) for prediction toxic activity of different data sets. Toxicology 2006; 217: 105-119.
40. Dong X, Jiang C, Hu H, Yan J, Chen J, Hu Y. QSAR study of Akt/protein kinase B (PKB) inhibitors using support vector machine. Eur. J. Med. Chem. 2009; 44: 4090-4097.
41. Clark RD, Fox PC. Statistical variation in progressive scrambling. J. Comput. Aided Mol. Des. 2004; 18: 563-576.
42. Baumann K. Cross-validation as the objective function for variable-selection techniques. TRAC-Trend Anal. Chem. 2003; 22: 395-406.
43. Kiralj R, Ferreira M. Basic validation procedures for regression models in QSAR and QSPR studies: theory and application. J. Braz. Chem. Soc. 2009; 20: 770-787.
44. Yun Y-H, Cao D-S, Tan M-L, Yan J, Ren D-B, Xu Q-S, Yu L, Liang Y-Z. A simple idea on applying large regression coefficient to improve the genetic algorithm-PLS for variable selection in multivariate calibration. Chemometr. Intell. Lab. Syst. 2014; 130: 76-83.
45. Leardi R. Genetic algorithms in chemometrics and chemistry: a review. J. Chemometr. 2001; 15: 559-569.
46. Niazi A, Leardi R. Genetic algorithms in chemometrics. J. Chemometr. 2012; 26: 345-351.
47. Goodarzi M, Heyden YV, Funar-Timofei S. Towards better understanding of feature-selection or reduction techniques for quantitative structure-activity relationship models. TRAC-Trend Anal. Chem. 2013; 42: 49-63.
48. Alexander G, Alexander T. Beware of Q2. J. Mol. Graph. Model. 2002; 20: 269-276.
49. Tropsha A, Gramatica P, Gombar VK. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR and Combinatorial Science 2003; 22: 69-77.
50. Shahlaei M. Descriptor selection methods in quantitative structure-activity relationship studies: a review study. Chem. Rev. 2013; 113: 8093-8103.
51. Rücker C, Rücker G, Meringer M. y-Randomization and its variants in QSPR/QSAR. J. Chem. Inf. Model. 2007; 47(6): 2345-2357.
52. Fox J-P. Randomized item response theory models. J. Educ. Behav. Stat. 2005; 30: 189-212.
53. Cao D-S, Liu S, Fan L, Liang Y-Z. QSAR analysis of the effects of OATP1B1 transporter by structurally diverse natural products using a particle swarm optimization-combined multiple linear regression approach. Chemometr. Intell. Lab. Syst. 2014; 130: 84-90.
54. Netzeva TI, Worth AP, Aldenberg T, Benigni R, Cronin MT, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. ATLA 2005; 33: 155-173.
55. Weaver S, Gleeson MP. The importance of the domain of applicability in QSAR modeling. J. Mol. Graph. Model. 2008; 26: 1315-1326.
56. Sahlin U, Jeliazkova N, Öberg T. Applicability domain dependent predictive uncertainty in QSAR regressions. Mol. Inform. 2014; 33: 26-35.
57. Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R. Comparison of different approaches to define the applicability domain of QSAR models. Molecules 2012; 17: 4791-4810.
58. Cao DS, Liang YZ, Xu QS, Li HD, Chen X. A new strategy of outlier detection for QSAR/QSPR. J. Comput. Chem. 2010; 31: 592-602.
59. Yan J, Huang JH, He M, Lu HB, Yang R, Kong B, Xu QS, Liang YZ. Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine. J. Sep. Sci. 2013; 36: 2464-2471.
60. Cao DS, Liang YZ, Xu QS, Yun YH, Li HD. Toward better QSAR/QSPR modeling: simultaneous outlier detection and variable selection using distribution of model features. J. Comput. Aided Mol. Des. 2011; 25: 67-80.
61. OECD-QSAR-07---ENV-JM-MONO. 2007; 2.
62. OECD-QSAR-04---ENV-JM-MONO. 2004; 24.
63. Mannhold R, Dross KP, Rekker RF. Drug lipophilicity in QSAR practice: I. A comparison of experimental with calculative approaches. Quantitative Structure-Activity Relationships 1990; 9: 21-28.
64. Vrakas D, Tsantili-Kakoulidou A, Hadjipavlou-Litina D. Exploring the consistency of logP estimation for substituted coumarins. QSAR and Combinatorial Science 2003; 22: 622-629.
65. Chrysanthakopoulos M, Koletsou A, Nicolaou I, Demopoulos VJ, Tsantili-Kakoulidou A. Lipophilicity studies on pyrrolyl-acetic acid derivatives. Experimental versus predicted logP values in relationship with aldose reductase inhibitory activity. QSAR and Combinatorial Science 2009; 28: 551-560.