Applicability domain for in silico models to achieve accuracy of experimental measurements

Journal of Chemometrics

Volumn 24, Issue 3-4, 2010, Pages 202-208

  Sushko, Iurii ^a   Novotarskyi, Sergii ^a   Körner, Robert ^a   Pandey, Anil Kumar ^a   Kovalishyn, Vasily V ^b   Prokopenko, Volodymyr V ^b   Tetko, Igor V ^a  

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b INSTITUTE OF BIOORGANIC CHEMISTRY AND PETROCHEMISTRY   (Ukraine)

Author keywords

Accuracy of predictions; Applicability domain; Physico chemical properties; Toxicity prediction

Indexed keywords

CLASSIFICATION (OF INFORMATION);

ACCURACY OF PREDICTION; APPLICABILITY DOMAIN; CLASSIFICATION MODELS; DOMAIN APPROACH; IN-SILICO MODELS; MUTAGENICITY; PERFORMANCE; PHYSICOCHEMICAL PROPERTY; REGRESSION AND CLASSIFICATION MODELS; TOXICITY PREDICTIONS;

FORECASTING;

EID: 77951080733     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1296     Document Type: Article

References (28)

1. Fawcett T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006; 27(8): 861-874.
2. Bi JK, Benett K. Regression Error Characteristic Curves.
3. Tetko IV, Bruneau P, Mewes H, Rohrer DC, Poda GI. Can we estimate the accuracy of ADME-Tox predictions? Drug Discov. Today 2006; 11(15-16): 700-707.
4. Mannhold R, Poda GI, Ostermann C, Tetko IV. Calculation of molecular lipophilicity: state-of-the-art and comparison of log P methods on more than 96 000 compounds. J. Pharm. Sci. 2009; 98(3): 861-893.
5. Kleinknecht RE. Error estimation in PLS latent variable structure. J. Chemom. 1996; 10(5-6): 687-695.
6. Denham MC. Prediction intervals in partial least squares. J. Chemom. 1997; 11(1): 39-52.
7. Faber K, Kowalski BR. Propagation of measurement errors for the validation of predictions obtained by principal component regression and partial least squares. J. Chemom. 1997; 11(3): 181-238.
8. Morsing T, Ekman C. Comments on construction of confidence intervals in connection with partial least squares. J. Chemom. 1998; 12(4): 295-299.
9. Seward JR, Sinks GD, Schultz TW. Reproducibility of toxicity across mode of toxic action in the Tetrahymena population growth impairment assay. Aquat. Toxicol. 2001; 53(1): 33-47.
10. Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, Oberg T, Todeschini R, Fourches D, Varnek A. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J. Chem. Inf. Model 2008; 48(9): 1733-1746.
11. Netzeva TI, Worth A, Aldenberg T, Benigni R, Cronin MTD, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA, Myatt G, Nikolova- Jeliazkova N, Patlewicz GY, Perkins R, Roberts D, Schultz T, Stanton DW, van de Sandt JJM, Tong W, Veith G, Yang C. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern. Lab. Anim. 2005; 33(2): 155-173.
12. Tetko IV. Associative neural network. Methods Mol. Biol. 2008; 458: 185-202.
13. Tetko IV. Neural network studies. 4. Introduction to associative neural networks. J. Chem. Inf. Comput. Sci. 2002; 42(3): 717-728.
14. Tetko IV, Tanchuk VY. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program. J. Chem. Inf. Comput. Sci. 2002; 42(5): 1136-1145.
15. Tetko IV, Livingstone DJ, Luik AI. Neural network studies. 1. Comparison of overfitting and overtraining. J. Chem. Inf. Comput. Sci. 1995; 35(5): 826-833.
16. Zhu H, Tropsha A, Fourches D, Varnek A, Papa E, Gramatica P, Oberg T, Dao P, Cherkasov A, Tetko IV. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. J. Chem. Inf. Model 2008; 48(4): 766-784.
17. Breiman L. Bagging predictors. Mach. Learn 1996; 24(2): 123-140.
18. Manallack DT, Tehan BG, Gancia E, Hudson BD, Ford MG, Livingstone DJ, Whitley DC, Pitt WR. A consensus neural network-based technique for discriminating soluble and poorly soluble compounds. J. Chem. Inf. Comput. Sci. 2003; 43(2): 674-679.
19. Sushko I, Novotarskyi S, Pandey AK, Korner R, Tetko I. Applicability Domain for Classification problems. Gesellschaft Deutscher Chemiker e. V.: Frankfurt am Main. 5. German Conference on Chemoinformatics. Book of abstracts. p. 80. Goslar, Germany, 2009.
20. Aptula AO, Roberts DW, Cronin MTD, Schultz TW. Chemistry-toxicity relationships for the effects of di and trihydroxybenzenes to Tetrahymena pyriformis. Chem. Res. Toxicol. 2005; 18(5): 844-854.
21. Dimitrov SD, Mekenyan OG, Sinks GD, Schultz TW. Global modeling of narcotic chemicals: ciliate and fish toxicity. J. Mol. Struct.: THEOCHEM. 2003; 622(1-2): 63-70.
22. Schultz TW, Netzeva TI, Cronin MTD. Evaluation of QSARS for ecotoxicity: a method for assigning quality and confidence. SAR QSAR Environ. Res. 2004; 15(5-6): 385-397.
23. Tetko IV, Poda G, Ostermann C, Mannhold R. Accurate in silico log P predictions: one can't embrace the unembraceable. QSAR Comb. Sci. 2009; 28(8): 845-849.
24. Tetko IV, Poda GI, Ostermann C, Mannhold R. Large-scale evaluation of log P predictors: local corrections may compensate insufficient accuracy and need of experimentally testing every other compound. Chem. Biodivers. 2009; 6(11): 1837-1844.
25. Mortelmans K, Zeiger E. The Ames Salmonella/microsome mutageni city assay. Mutat. Res.: Fund. Mol. Mech. Mutagen. 2000; 455(1-2): 29-60.
26. Hansen K, Mika S, Schroeter T, Sutter A, ter Laak A, Steger-Hartmann T, Heinrich N, Muller K. Benchmark data set for in silico prediction of Ames mutagenicity. J. Chem. Inf. Model 2009; 49(9): 2077-2081.
27. Piegorsch W, Zeiger E. Measuring intra-assay agreement for the Ames Salmonella assay. Lect. Notes Med. Inform. 43: 35-41.
28. Benigni R, Giuliani A. Computer-assisted analysis of interlaboratory Ames test variability. J. Toxicol. Environ. Health A: Curr Iss. 1988; 25(1): 135.