Review
Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
H. Li,
C.W. Yap,
C.Y. Ung,
Y. Xue,
Z.R. Li,
L.Y. Han,
H.H. Lin,
Y.Z. Chen,
H. Li
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Search for more papers by this authorC.W. Yap
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Search for more papers by this authorC.Y. Ung
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Search for more papers by this authorY. Xue
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
College of Chemistry, Sichuan University, Chengdu 610064, P.R. China
Search for more papers by this authorZ.R. Li
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
College of Chemistry, Sichuan University, Chengdu 610064, P.R. China
Search for more papers by this authorL.Y. Han
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Search for more papers by this authorH.H. Lin
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Search for more papers by this authorCorresponding Author
Y.Z. Chen
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore. Telephone: 65-6874-6877; Fax: 65-6774-6756Search for more papers by this authorH. Li,
C.W. Yap,
C.Y. Ung,
Y. Xue,
Z.R. Li,
L.Y. Han,
H.H. Lin,
Y.Z. Chen,
H. Li
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Search for more papers by this authorC.W. Yap
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Search for more papers by this authorC.Y. Ung
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Search for more papers by this authorY. Xue
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
College of Chemistry, Sichuan University, Chengdu 610064, P.R. China
Search for more papers by this authorZ.R. Li
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
College of Chemistry, Sichuan University, Chengdu 610064, P.R. China
Search for more papers by this authorL.Y. Han
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Search for more papers by this authorH.H. Lin
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Search for more papers by this authorCorresponding Author
Y.Z. Chen
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore. Telephone: 65-6874-6877; Fax: 65-6774-6756Search for more papers by this authorAbstract
Computational methods for predicting compounds of specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) property are useful for facilitating drug discovery and evaluation. Recently, machine learning methods such as neural networks and support vector machines have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic and ADMET property. These methods are particularly useful for compounds of diverse structures to complement QSAR methods, and for cases of unavailable receptor 3D structure to complement structure-based methods. A number of studies have demonstrated the potential of these methods for predicting such compounds as substrates of P-glycoprotein and cytochrome P450 CYP isoenzymes, inhibitors of protein kinases and CYP isoenzymes, and agonists of serotonin receptor and estrogen receptor. This article is intended to review the strategies, current progresses and underlying difficulties in using machine learning methods for predicting these protein binders and as potential virtual screening tools. Algorithms for proper representation of the structural and physicochemical properties of compounds are also evaluated. © 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 96: 2838–2860, 2007
REFERENCES
- 1 Drews J. 2000. Drug discovery: A historical perspective. Science 287: 1960–1964.
- 2 Park BK, Kitteringham NR, Powell H, Pirmohamed M. 2000. Advances in molecular toxicology—Towards understanding idiosyncratic drug toxicity. Toxicology 153: 39–60.
- 3 Caldwell J, Gardner I, Swales N. 1995. An introduction to drug disposition: the basic principles of absorption, distribution, metabolism, and excretion. Toxicol Pathol 23: 102–114.
- 4 White RE. 2000. High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery. Annu Rev Pharmacol Toxicol 40: 133–157.
- 5 Ekins S, Ring BJ, Grace J, McRobie-Belle DJ, Wrighton SA. 2000. Present and future in vitro approaches for drug metabolism. J Pharmacol Toxicol Methods 44: 313–324.
- 6 Zheng CJ, Han LY, Yap CW, Ji ZL, Cao ZW, Chen YZ. 2006. Therapeutic targets: Progress of their exploration and investigation of their characteristics. Pharmacol Rev 58: 259–279.
- 7 Zheng CJ, Sun LZ, Han LY, Ji ZL, Chen XYZC. 2004. Drug ADME-associated protein database as a resource for facilitating pharmacogenomics research. Drug Dev Res 62: 134–142.
- 8 Ji ZL, Han LY, Yap CW, Sun LZ, Chen X, Chen YZ. 2003. Drug adverse reaction target database (DART): Proteins related to adverse drug reactions. Drug Saf 26: 685–690.
- 9 Hansch C, Leo A, Mekapati SB, Kurup A. 2004. QSAR and ADME. Bioorg Med Chem 12: 3391–3400.
- 10 Katritzky AR, Karelson M, Lobanov V. 1997. QSPR as a means of predicting and understanding chemical and physical properties in terms of structure. Pure Appl Chem 69: 245–248.
- 11 Manallack DT, Livingstone DJ. 1999. Neural networks in drug discovery: Have they lived up to their promise? Eur J Med Chem 34: 195–208.
- 12 van de Waterbeemd H, Gifford E. 2003. ADMET in silico modelling: Towards prediction paradise? Nat Rev Drug Discov 2: 192–204.
- 13 Trotter MWB, Holden SB. 2003. Support vector machines for ADME property classification. QSAR Combinatorial Sci 22: 533–548.
- 14 Burbidge R, Trotter M, Buxton B, Holden S. 2001. Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Comput Chem 26: 5–14.
- 15 Yao X, Liu H, Zhang R, Liu M, Hu Z, Panaye A, Doucet JP, Fan B. 2005. QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines. Mol Pharm 2: 348–356.
- 16 Ng C, Xiao Y, Putnam W, Lum B, Tropsha A. 2004. Quantitative structure–pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k-nearest-neighbor and partial least-square analysis methods. J Pharm Sci 93: 2535–2544.
- 17 Yap CW, Chen YZ. 2004. Quantitative structure–pharmacokinetic relationships for drug distribution properties by using general regression neural network. J Pharm Sci 94: 153–168.
- 18 Harper G, Bradshaw J, Gittins JC, Green DV, Leach AR. 2001. Prediction of biological activity for high-throughput screening using binary kernel discrimination. J Chem Inf Comput Sci 41: 1295–1300.
- 19 Jorissen RN, Gilson MK. 2005. Virtual screening of molecular databases using a support vector machine. J Chem Inf Model 45: 549–561.
- 20 Glick M, Jenkins JL, Nettles JH, Hitchings H, Davies JW. 2006. Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers. J Chem Inf Model 46: 193–200.
- 21 Ghosh S, Nie A, An J, Huang Z. 2006. Structure-based virtual screening of chemical libraries for drug discovery. Curr Opin Chem Biol 10: 194–202.
- 22 Shoichet BK. 2004. Virtual screening of chemical libraries. Nature 432: 862–865.
- 23 Oprea TI, Matter H. 2004. Integrating virtual screening in lead discovery. Curr Opin Chem Biol 8: 349–358.
- 24 Lengauer T, Lemmen C, Rarey M, Zimmermann M. 2004. Novel technologies for virtual screening. Drug Discov Today 9: 27–34.
- 25 Xue Y, Li ZR, Yap CW, Sun LZ, Chen X, Chen YZ. 2004. Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. J Chem Inf Comput Sci 44: 1630–1638.
- 26 Li H, Yap CW, Ung CY, Xue Y, Cao ZW, Chen YZ. 2005. Effect of selection of molecular descriptors on the prediction of blood–brain barrier penetrating and nonpenetrating agents by statistical learning methods. J Chem Inf Model 45: 1376–1384.
- 27 Bergstrom CA, Norinder U, Luthman K, Artursson P. 2003. Molecular descriptors influencing melting point and their role in classification of solid drugs. J Chem Inf Comput Sci 43: 1177–1185.
- 28 Perez JJ. 2005. Managing molecular diversity. Chem Soc Rev 34: 143–152.
- 29 Willett P, Barnard JM, Downs GM. 1998. Chemical similarity searching. J Chem Inf Comput Sci 38: 983–996.
- 30 Molnar L, Keseru GM. 2002. A neural network based virtual screening of cytochrome P450 3A4 inhibitors. Bioorg Med Chem Lett 12: 419–421.
- 31 Potter T, Matter H. 1998. Random or rational design? Evaluation of diverse compound subsets from chemical structure databases. J Med Chem 41: 478–488.
- 32 Pacher P, Kecskemeti V. 2004. Trends in the development of new antidepressants. Is there a light at the end of the tunnel? Curr Med Chem 11: 925–943.
- 33 Stichtenoth DO, Frolich JC. 2003. The second generation of COX-2 inhibitors: What advantages do the newest offer? Drugs 63: 33–45.
- 34 Hochberg MC. 2005. COX-2 selective inhibitors in the treatment of arthritis: A rheumatologist perspective. Curr Top Med Chem 5: 443–448.
- 35 Linkins LA, Weitz JI. 2005. Pharmacology and clinical potential of direct thrombin inhibitors. Curr Pharm Des 11: 3877–3884.
- 36 Francis CW. 2005. Direct thrombin inhibitors for treatment of heparin induced thrombocytopenia, deep vein thrombosis and atrial fibrillation. Curr Pharm Des 11: 3931–3941.
- 37 Paoli G, Merlini PA, Ardissino D. 2005. Direct thrombin inhibitors for the treatment of acute coronary syndromes and during percutaneous coronary interventions. Curr Pharm Des 11: 3919–3929.
- 38 Ribeiro S, Horuk R. 2005. The clinical potential of chemokine receptor antagonists. Pharmacol Ther 107: 44–58.
- 39
Tsibris AM,
Kuritzkes DR.
2006.
Chemokine antagonists as therapeutics: Focus on HIV-1.
Annu Rev Med:
doi:10.1146/annurev.med.1158.080105.102908.
doi:10.1146/annurev.med.1158.080105.102908 Google Scholar
- 40 Spaltenstein A, Kazmierski WM, Miller JF, Samano V. 2005. Discovery of next generation inhibitors of HIV protease. Curr Top Med Chem 5: 1589–1607.
- 41 Fabbro D, Ruetz S, Buchdunger E, Cowan-Jacob SW, Fendrich G, Liebetanz J, Mestan J, O'Reilly T, Traxler P, Chaudhuri B, Fretz H, Zimmermann J, Meyer T, Caravatti G, Furet P, Manley PW. 2002. Protein kinases as targets for anticancer agents: From inhibitors to useful drugs. Pharmacol Ther 93: 79–98.
- 42 Kumar R, Singh VP, Baker KM. 2006. Kinase inhibitors for cardiovascular disease. J Mol Cell Cardiol doi:10.1016/j.yjmcc.2006.09.005.
- 43 Rotella DP. 2002. Phosphodiesterase 5 inhibitors: Current status and potential applications. Nat Rev Drug Discov 1: 674–682.
- 44 Coelingh BHJ. 2004. Are all estrogens the same? Maturitas 47: 269–275.
- 45 Oh WK. 2002. The evolving role of estrogen therapy in prostate cancer. Clin Prostate Cancer 1: 81–89.
- 46 Behl C. 2002. Oestrogen as a neuroprotective hormone. Nat Rev Neurosci 3: 433–442.
- 47 Lissin LW, Cooke JP. 2000. Phytoestrogens and cardiovascular health. J Am Coll Cardiol 35: 1403–1410.
- 48 Safe SH, Pallaroni L, Yoon K, Gaido K, Ross S, Saville B, McDonnellc D. 2001. Toxicology of environmental estrogens. Reprod Fertil Dev 13: 307–315.
- 49 Hileman B. 1997. Hormone disrupter research expands. Chem Eng News 75: 24.
- 50 van de Waterbeemd H, Gifford E. 2003. ADMET in silico modelling: Towards prediction paradise? Nat Rev Drug Discov 2: 192–204.
- 51 Li AP. 2001. Screening for human ADME/Tox drug properties in drug discovery. Drug Discov Today 6: 357–366.
- 52 Keseru GM. 2001. A virtual high throughput screen for high affinity cytochrome P450cam substrates. Implications for in silico prediction of drug metabolism. J Comput Aided Mol Des 15: 649–657.
- 53 Li AP, Kaminski DL, Rasmussen A. 1995. Substrates of human hepatic cytochrome P450 3 A4. Toxicology 104: 1–8.
- 54 Ekins S, de Groot MJ, Jones JP. 2001. Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites. Drug Metab Dispos 29: 936–944.
- 55 Smith DA, Ackland MJ, Jones BC. 1997. Properties of cytochrome P450 isoenzymes and their substrates. Part 1. Active site characteristics. Drug Discov Today 2: 406–414.
- 56 de Groot MJ, Ekins S. 2002. Pharmacophore modeling of cytochromes P450. Adv Drug Deliv Rev 54: 367–383.
- 57 Drews J. 2000. Drug discovery: A historical perspective. Science 287: 1960–1964.
- 58 White RE. 2000. High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery. Annu Rev Pharmacol Toxicol 40: 133–157.
- 59 Tirona RG, Kim RB. 2005. Nuclear receptors and drug disposition gene regulation. J Pharm Sci 94: 1169–1186.
- 60 Ekins S. 2004. Predicting undesirable drug interactions with promiscuous proteins in silico. Drug Discov Today 9: 276–285.
- 61 Ung CY, Li H, Yap CW, Chen YZ. 2006. In silico prediction of pregnane X receptor activators by machine learning approaches. Mol Pharmacol doi:10.1124/mol.106.027623.
- 62 Schmitt L, Tampe R. 2002. Structure and mechanism of ABC transporters. Curr Opin Struct Biol 12: 754–760.
- 63 Ambudkar SV, Dey S, Hrycyna CA, Ramachandra M, Pastan I, Gottesman MM. 1999. Biochemical, cellular, and pharmacological aspects of the multidrug transporter. Annu Rev Pharmacol Toxicol 39: 361–398.
- 64 Kim RB, Fromm MF, Wandel C, Leake B, Wood AJ, Roden DM, Wilkinson GR. 1998. The drug transporter P-glycoprotein limits oral absorption and brain entry of HIV-1 protease inhibitors. J Clin Invest 101: 289–294.
- 65 Bakken GA, Jurs PC. 2000. Classification of multidrug-resistance reversal agents using structure-based descriptors and linear discriminant analysis. J Med Chem 43: 4534–4541.
- 66 Penzotti JE, Lamb ML, Evensen E, Grootenhuis PD. 2002. A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein. J Med Chem 45: 1737–1740.
- 67 Todeschini R, Consonni V, Mauri A, Pavan M. 2005. DRAGON. Version 5.3 ed.
- 68 Hall LH, Kellogg GE, Haney DN. 2002. Molconn-Z. Version 4.05+ ed.: eduSoft, LC.
- 69 Wegner JK. 2005. JOELib/JOELib2. ed.
- 70 Hemmer MC, Steinhauer V, Gasteiger J. 1999. Deriving the 3D structure of organic molecules from their infrared spectra. Vibrational Spectrosc 19: 151–164.
- 71 Rücker G, Rücker C. 1993. Counts of all walks as atomic and molecular descriptors. J Chem Inf Comput Sci 33: 683–695.
- 72 Schuur JH, Setzer P, Gasteiger J. 1996. The coding of the three-dimensional structure of molecules by molecular transforms and its application to structure–spectra correlations and studies of biological activity. J Chem Inf Comput Sci 36: 334–344.
- 73 Pearlman RS, Smith KM. 1999. Metric validation and the receptor-relevant subspace concept. J Chem Inf Comput Sci 39: 28–35.
- 74 Bravi G, Gancia E, Mascagni P, Pegna M, Todeschini R, Zaliani A. 1997. MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: A comparative 3D QSAR study in a series of steroids. J Comput Aided Mol Des 11: 79–92.
- 75 Galvez J, Garcia R, Salabert MT, Soler R. 1994. Charge indexes. New topological descriptors. J Chem Inf Comput Sci 34: 520–525.
- 76 Consonni V, Todeschini R, Pavan M. 2002. Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 42: 682–692.
- 77 Randic M. 1975. Graph theoretical approach to local and overall aromaticity of benzenoid hydrocarbons. Tetrahedron 31: 1477–1481.
- 78 Randic M. 1995. Molecular profiles. Novel geometry-dependent molecular descriptors. New J Chem 19: 781–791.
- 79 Kier LB, Hall LH. 1999. Molecular structure description: The electrotopological state. Har/Cdr edition, San Diego: Academic Press.
- 80 Platts JA, Butina D, Abraham MH, Hersey A. 1999. Estimation of molecular free energy relation descriptors using a group contribution approach. J Chem Inf Comput Sci 39: 835–845.
- 81 Li H, Ung CY, Yap CW, Xue Y, Li ZR, Cao ZW, Chen YZ. 2005. Prediction of genotoxicity of chemical compounds by statistical learning methods. Chem Res Toxicol 18: 1071–1080.
- 82 Yap CW, Chen YZ. 2005. Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines. J Chem Inf Model 45: 982–992.
- 83 Xue Y, Yap CW, Sun LZ, Cao ZW, Wang JF, Chen YZ. 2004. Prediction of p-glycoprotein substrates by support vector machine approach. J Chem Inf Comput Sci 44: 1497–1505.
- 84 Hosmer DW, Lemeshow S. 1989. Applied logistic regression. 2nd ed., New York: Wiley.
- 85 Huberty CJ. 1994. Applied discriminant analysis. 1st ed., New York: John Wiley & Sons.
- 86 Fix E, Hodges JL. 1951. Discriminatory analysis: Non-parametric discrimination: Consistency properties. 1st ed., Texas: USAF School of Aviation Medicine. pp 261–279.
- 87 Chen B, Harrison RF, Pasupa K, Willett P, Wilton DJ, Wood DJ, Lewell XQ. 2006. Virtual screening using binary kernel discrimination: effect of noisy training data and the optimization of performance. J Chem Inf Model 46: 478–486.
- 88 Wilton DJ, Harrison RF, Willett P, Delaney J, Lawson K, Mullier G. 2006. Virtual screening using binary kernel discrimination: analysis of pesticide data. J Chem Inf Model 46: 471–477.
- 89 Aleksander I, Morton H. 1995. An introduction to neural computing. 2nd edition, London: International Thomson Computer Press.
- 90 Specht DF. 1990. Probabilistic neural networks. Neural Netw 3: 109–118.
- 91 Parzen E. 1962. On estimation of a probability density function and mode. Ann Math Stat 33: 1065–1076.
- 92 Vapnik VN. 1995. The nature of statistical learning theory. 1st ed., New York: Springer.
- 93 Quinlan JR. 1993. C4.5: Programs for machine learning. 1st ed., San Mateo, CA: Morgan Kaufmann.
- 94 Weiss SM, Kulikowski CA. 1991. Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems. 1st ed., San Francisco, CA, USA: Morgan Kaufmann Publishers, Inc.
- 95
Shao J,
Tu D.
1995.
The Jackknife and Bootstrap.
1st ed.,
New York, NY, USA: Springer.
10.1007/978-1-4612-0795-5 Google Scholar
- 96 Romesburg C. 2004. Cluster analysis for researchers. 1st ed., North Carolina: Lulu Press.
- 97 Lucasius CB, Kateman G. 1993. Understanding and using genetic algorithms. Part 1. Concepts, properties and context. Chemometr Intell Lab 19: 1–33.
- 98 Guyon I, Weston J, Barnhill S, Vapnik V. 2002. Gene selection for cancer classification using support vector machines. Mach Learn 46: 389–422.
- 99 Sutter JM, Kalivas JH. 1993. Comparison of forward selection, backward elimination, and generalized simulated annealing for variable selection. Microchem J 47: 60–66.
- 100 Liu HX, Zhang RS, Yao XJ, Liu MC, Hu ZD, Fan BT. 2004. QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-Diarylimidazoles based on support vector machines. J Comput Aided Mol Des 18: 389–399.
- 101 Li H, Ung CY, Yap CW, Xue Y, Li ZR, Chen YZ. 2006. Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods. J Mol Graph Model 25: 313–323.
- 102 Yap CW, Cai CZ, Xue Y, Chen YZ. 2004. Prediction of torsade-causing potential of drugs by support vector machine approach. Toxicol Sci 79: 170–177.
- 103 Kohavi R, John GH. 1997. Wrappers for feature subset selection. Artif Intell Med 97: 273–324.
- 104 Yu H, Yang J, Wang W, Han J. 2003. IEEE Computer Society Bioinformatics Conference (CSB'03), Stanford, California, August 11–14. pp 220–228.
- 105 Gramatica P, Pilutti P, Papa E. 2004. Validated QSAR prediction of OH tropospheric degradation of VOCs: Splitting into training-test sets and consensus modeling. J Chem Inf Comput Sci 44: 1794–1802.
- 106 Izrailev S, Agrafiotis DK. 2004. A method for quantifying and visualizing the diversity of QSAR models. J Mol Graph Mod 22: 275–284.
- 107 Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H. 2000. Assessing the accuracy of prediction algorithms for classification: An overview. Bioinformatics 16: 412–424.
- 108 Grover M, Singh B, Bakshi M, Singh S. 2000. Quantitative structure–property relationships in pharmaceutical research, Part 2. Pharmaceut Sci Technol Today 3: 50–57.
- 109 Xue CX, Zhang RS, Liu HX, Liu MC, Hu ZD, Fan BT. 2004. Support vector machines-based quantitative structure–property relationship for the prediction of heat capacity. J Chem Inf Comput Sci 44: 1267–1274.
- 110 Hoffman B, Cho SJ, Zheng W, Wyrick S, Nichols DE, Mailman RB, Tropsha A. 1999. Quantitative structure–activity relationship modeling of dopamine D(1) antagonists using comparative molecular field analysis, genetic algorithms-partial least-squares, and k nearest neighbor methods. J Med Chem 42: 3217–3226.
- 111 Xue CX, Zhang RS, Liu HX, Yao XJ, Liu MC, Hu ZD, Fan BT. 2004. QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine. J Chem Inf Comput Sci 44: 1693–1700.
- 112 Turner JV, Maddalena DJ, Cutler DJ. 2005. Pharmacokinetic parameter prediction from drug structure using artificial neural networks. Int J Pharm 270: 209–219.
- 113 Lepp Z, Kinoshita T, Chuman H. 2006. Screening for new antidepressant leads of multiple activities by support vector machines. J Chem Inf Model 46: 158–167.
- 114 Li H, Yap CW, Xue Y, Li ZR, Ung CY, Han LY, Chen YZ. 2006. Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic or toxicological properties of pharmaceutical agents. Drug Dev Res 66: 245–259.
- 115 Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A. 2006. New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching. J Chem Inf Model 46: 462–470.
- 116 Franke L, Byvatov E, Werz O, Steinhilber D, Schneider P, Schneider G. 2005. Extraction and visualization of potential pharmacophore points using support vector machines: Application to ligand-based virtual screening for COX-2 inhibitors. J Med Chem 48: 6997–7004.
- 117 PubMed. ed.: National Library of Medicine.
- 118 MICROMEDEX. Edition expires 12/2003. MICROMEDEX. 1st ed., Greenwood Village, CO: MICROMEDEX.
- 119 Bethesda. 2001. AHFS drug information. 1st ed., American Society of Health-System Pharmacists, Inc.
- 120 Roth BL, Kroeze WK, Patel S, Lopez E. 2000. The multiplicity of serotonin receptors: Uselessly diverse molecules or an embarrasment of riches? The Neuroscientist 6: 252–262.
- 121 Zhang J-W, Aizawa M, Amari S, Iwasawa Y, Nakano T, Nakata K. 2004. Development of KiBank, a database supporting structure-based drug design. Comput Biol Chem 28: 401–407.
- 122 PubChem. ed.: National Library of Medicine.
- 123 Chen X, Ji ZL, Zhi DG, Chen YZ. 2002. CLiBE: A database of computed ligand binding energy for ligand-receptor complexes. Comput Chem 26: 661–666.
- 124 Veropoulos K, Campbell C, Cristianini N. 1999. UCAI99 Sweden: Morgan Kaufmann. pp 55–60.
- 125 Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares MJ, Haussler D. 2000. Knowledge-based analysis of microarray gene expression data using support vector machines. Proc Natl Acad Sci USA 97: 262–267.
- 126 Al-Shahib A, Breitling R, Gilbert D. 2005. Feature selection and the class imbalance problem in predicting protein function from sequence. Appl Bioinform 4: 195–203.
- 127 Furlanello C, Serafini M, Merler S, Jurman G. 2003. An accelerated procedure for recursive feature ranking on microarray data. Neural Netw 16: 641–648.
- 128 Stanton DT. 2003. On the physical interpretation of QSAR models. J Chem Inf Comput Sci 43: 1423–1433.
