Potency-Directed Similarity Searching Using Support Vector Machines

Chemical Biology and Drug Design

Volumn 77, Issue 1, 2011, Pages 30-38

  Wassermann, Anne M ^a   Heikamp, Kathrin ^a   Bajorath, Jürgen ^b  

^a B IT   (Germany)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

Compound classification; Compound potency; Kernel functions; Molecular similarity; Similarity searching; Support vector machines

Indexed keywords

ARTICLE; CALCULATION; CONTROLLED STUDY; DATA BASE; DRUG INFORMATION; DRUG POTENCY; HIGH THROUGHPUT SCREENING; KERNEL METHOD; MEDICAL RESEARCH; PRIORITY JOURNAL; REFERENCE DATABASE; SAMPLE SIZE; SUPPORT VECTOR MACHINE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATABASES, FACTUAL; DRUG EVALUATION, PRECLINICAL; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; INHIBITORY CONCENTRATION 50; LIGANDS; LINEAR MODELS; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 78650095648     PISSN: 17470277     EISSN: 17470285     Source Type: Journal    
DOI: 10.1111/j.1747-0285.2010.01059.x     Document Type: Article

References (26)

1. Geppert H., Vogt M., Bajorath J. (2010) Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model;50:205-216.
2. Eckert H., Bajorath J. (2007) Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov Today;12:225-233.
3. Esposito E.X., Hopfinger A.J., Madura J.D. (2004) Methods for applying the quantitative structure-activity relationship paradigm. Methods Mol Biol;275:131-214.
4. Vogt I., Bajorath J. (2007) Analysis of a high-throughput screening data set using potency-scaled molecular similarity algorithms. J Chem Inf Model;47:367-375.
5. Vapnik V.N. (2000) The Nature of Statistical Learning Theory, 2nd edn. New York: Springer.
6. Boser B.E., Guyon I.M., Vapnik V. (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual Workshop on Computational Learning Theory. Pittsburgh, Pennsylvania. New York: ACM; p. 144-152.
7. Müller K.-R., Mika S., Rätsch G., Tsuda K., Schölkopf B. (2001) An introduction to kernel-based learning algorithms. IEEE Neural Netw;12:181-201.
8. Schölkopf B., Smola A. (2002) Learning with Kernels. Cambridge, MA: MIT Press.
9. 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.
10. Warmuth M.K., Liao J., Rätsch G., Mathieson M., Putta S., Lemmen C. (2003) Active learning with support vector machines in the drug discovery process. J Chem Inf Comput Sci;43:667-673.
11. Jorissen R.N., Gilson M.K. (2005) Virtual screening of molecular databases using a support vector machine. J Chem Inf Model;45:549-561.
12. Wassermann A.M., Geppert H., Bajorath J. (2009) Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. J Chem Inf Model;49:582-592.
13. Agarwal S., Dugar D., Sengupta S. (2010) Ranking chemical structures for drug discovery: a new machine learning approach. J Chem Inf Model;50:716-731.
14. Jacob L., Vert J.-P. (2008) Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics;24:2149-2156.
15. Geppert H., Humrich J., Stumpfe D., Gärtner T., Bajorath J. (2009) Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors. J Chem Inf Model;49:767-779.
16. Wassermann A.M., Geppert H., Bajorath J. (2009) Ligand prediction for orphan targets using support vector machines and various target-ligand kernels is dominated by nearest neighbor effects. J Chem Inf Model;49:2155-2167.
17. Ning X., Rangwala H., Karypis G. (2009) Multi-assay-based structure-activity relationship models: improving structure-activity relationship models by incorporating activity information from related targets. J Chem Inf Model;49:2444-2456.
18. Wale N., Karypis G. (2009) Target fishing for chemical compounds using target-ligand activity data and ranking based methods. J Chem Inf Model;49:2190-2201.
19. Michielan L., Terfloth L., Gasteiger J., Moro S. (2009) Comparison of multilabel and single-label classification applied to the prediction of the isoform specificity of cytochrome P450 substrates. J Chem Inf Model;49:2588-2605.
20. Michielan L., Stephanie F., Terfloth L., Hristozov D., Cacciari B., Klotz K.-N., Spalluto G., Gasteiger J., Moro S. (2009) Exploring potency and selectivity receptor antagonist profiles using a multilabel classification approach: the human adenosine receptors as a key study. J Chem Inf Model;49:2820-2836.
21. Rogers D., Hahn M. (2010) Extended-connectivity fingerprints. J Chem Inf Model;50:742-754.
22. Ralaivola L., Swamidass S.J., Saigo H., Baldi P. (2005) Graph kernels for chemical informatics. Neural Netw;18:1093-1110.
23. Joachims T. (1999) Making large-scale SVM learning practical. In: Schölkopf B., Burges C., Smola A., editors. Advances in Kernel Methods - Support Vector Learning. Cambridge, MA: MIT-Press: p. 169-184.
24. Wang Y., Bajorath J. (2010) Advanced fingerprint methods for similarity searching: balancing molecular complexity effects. Comb Chem High Throughput Screen;13:220-228.
25. Wang Y., Bajorath J. (2008) Balancing the influence of molecular complexity on fingerprint similarity searching. J Chem Inf Model;48:75-84.
26. Parker C.N., Shamu C.E., Kraybill B., Austin C.P., Bajorath J. (2006) Measure, mine, model, and manipulate: the future for HTS and chemoinformatics? Drug Discov Today;11:863-865.