Trade-off between accuracy and interpretability for predictive in silico modeling

Future Medicinal Chemistry

Volumn 3, Issue 6, 2011, Pages 647-663

  Johansson, Ulf ^a   Sönströd, Cecilia ^a   Norinder, Ulf ^b   Boström, Henrik ^c  

^a UNIVERSITY OF BORÅS   (Sweden)

^b ASTRAZENECA   (United Kingdom)

^c STOCKHOLM UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; COMPUTER MODEL; COMPUTER PREDICTION; DRUG DESIGN; DRUG RESEARCH; MACHINE LEARNING; PRIORITY JOURNAL;

EID: 79955967718     PISSN: 17568919     EISSN: 17568927     Source Type: Journal    
DOI: 10.4155/fmc.11.23     Document Type: Article

References (24)

1. van de Waterbeemd H, Gifford E. Admet in silico modeling: towards prediction paradise? Nat. Rev. Drug. Discov. 2(3), 192-204 (2003). (Pubitemid 37361664)
2. Wagener M, van Geerestein V. Potential drugs and nondrugs: prediction and identification of important structural features. J. Chem. Inf. Comput. Sci. 40(2), 280-292 (2000).
3. Bohanec M, Bratko I. Trading accuracy for simplicity in decision trees. Machine Learning 15(3), 223-250 (1994).
4. Plate T. Accuracy versus interpretability in flexible modeling: implementing a tradeoff using Gaussian process models. Behaviourmetrika 26(1), 29-50 (1999).
5. Witten IH, Frank E. Data Mining - Practical Machine Learning Tools and Techniques. Elsevier, San Francisco, CA, USA (2005).
6. Quinlan JR. Induction of decision trees. Machine Learning 11(1), 81-106 (1986).
7. Cohen W. Fast effective rule induction. Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, CA, USA 1995.
8. Johansson U, Sönströd C, Löfström T, Boström H. Chipper - a novel algorithm for concept description. Scandinavian Conference on Artificial Intelligence, Stockholm, Sweden, 26-28 May 2008.
9. Hansen LK, Salamon P. Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993-1001 (1990).
10. Krogh A, Vedelsby J. Neural network ensembles, cross validation, and active learning. In: Advances in Neural Information Processing Systems (Volume 2).Morgan Kaufmann, San Mateo, CA, USA 650-659 (1995).
11. Breiman L. Bagging predictors. Machine Learning 24, 123-140 (1996). (Pubitemid 126724382)
12. Schapire R. The strength of weak learnability. Machine Learning 5(2), 197-227 (1990).
13. Caruana R, Niculescu-Mizil A. An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA, 25-29 June 2006.
14. Freund Y, Schapire R. Experiments with a new boosting algorithm. Proceedings of the 13th International Conference on Machine Learning, Bari, Italy, 4-6 July 1996.
15. Breiman L. Random forests. Machine Learning 45, 5-32 (2001). (Pubitemid 32933532)
16. Fawcett T. Using rule sets to maximize ROC performance. Proceedings of the 2001 International Conference on Machine Learning 131-138, Washington, DC, USA (2001).
17. Bruneau P. Search for predictive generic model of aqueous solubility using bayesian neural nets. J. Chem. Inf. Comput. Sci. 41, 1605-1616 (2001).
18. Rodgers D, Hahn M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742-754 (2010).
19. Faulon Jl, Collins MJ, Carr RD. The signature molecular descriptor. 4. Canonizing molecules using extended valence sequences. J. Chem. Inf. Comput. Sci. 44, 427-436 (2004).
20. Hou TJ, Zhang W, Xia K, Qiao XB, Xu XJ. ADME evaluation in drug discovery. 5. Correlation of caco-2 permeation with simple molecular properties. J. Chem. Inf. Comput. Sci. 44, 1585-1600 (2004).
21. Mittal RR, McKinnon RA, Sorich MJ. Comparison data sets for benchmarking QSAR methodologies in lead optimization. J. Chem. Inf. Model. 49, 1810-1820 (2009).
22. Bruce CL, Melville Jl, Pickett SD, Hirst JD. Contemporary QSAR classifiers compared. J. Chem. Inf. Model. 47, 219-227 (2007). (Pubitemid 46225577)
23. Demšar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1-30 (2006). (Pubitemid 43022939)
24. Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32, 675-701 (1937).