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Title: Does drug-target have a likeness?
Authors: Xu, H.
Fang, Y.
Yao, L.
Chen, Y. 
Chen, X.
Keywords: Drug target
Human genome
Statistical learning
Support vector machine
Issue Date: 2007
Citation: Xu, H., Fang, Y., Yao, L., Chen, Y., Chen, X. (2007). Does drug-target have a likeness?. Methods of Information in Medicine 46 (3) : 360-366. ScholarBank@NUS Repository.
Abstract: Objective: The discovery of new targets that are sufficiently robust to yield marketable therapeutics is an enormous challenge. Conventional target identification approaches are disease-dependent, which require heavy experimental workload and comprehensive domain knowledge. In this work, we propose that a disease-independent property of proteins, "drug-target likeness", can be explored to facilitate the genomic scale target screening in the post-genomic age. Methods: A Support Vector Machine (SVM) classifier was trained to recognize target and non-target protein sequences compiled from the Therapeutic Target Database, DrugBank, and PFam. Protein sequences are encoded by their composition, transition and distribution features of residues and Gaussian kernel function was used in SVM classification. Results: SM with a fine-tuned kernel width records 66.4 ± 5.1% of sensitivity and 97.2 ± 0.6% of specificity, corresponding to an overall target prediction accuracyof 94.4 ± 0.8%. Conclusions: Though primitive, these results suggest that, similar to the "drug likeness" for small chemicals, their binding partners, drug targets, also display shared features which are reflected in their sequences and can be captured by statistical learning approaches. Further research on how to accurately and interpretably measure the likeness of protein being a drug target is promising. Inspired by the progress of "drug likeness" studies, advances in protein descriptors, statistical learning algorithms and more comprehensive and accurate gold-standard data set from disease biology research may help to further define the "drug-target likeness" property of proteins. © 2007 Schattauer GmbH.
Source Title: Methods of Information in Medicine
ISSN: 00261270
DOI: 10.1160/ME0425
Appears in Collections:Staff Publications

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