| Substructure-based support vector machine classifiers for prediction of adverse effects in diverse classes of drugs. | |
| | |
MedLine Citation:
|
PMID: 17125188 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
|
Unforeseen adverse effects exhibited by drugs contribute heavily to late-phase failure and even withdrawal of marketed drugs. Torsade de pointes (TdP) is one such important adverse effect, which causes cardiac arrhythmia and, in some cases, sudden death, making it crucial for potential drugs to be screened for torsadogenicity. The need to tap the power of computational approaches for the prediction of adverse effects such as TdP is increasingly becoming evident. The availability of screening data including those in organized databases greatly facilitates exploration of newer computational approaches. In this paper, we report the development of a prediction method based on a support machine vector algorithm. The method uses a combination of descriptors, encoding both the type of toxicophore as well as the position of the toxicophore in the drug molecule, thus considering both the pharmacophore and the three-dimensional shape information of the molecule. For delineating toxicophores, a novel pattern-recognition method that utilizes substructures within a molecule has been developed. The results obtained using the hybrid approach have been compared with those available in the literature for the same data set. An improvement in prediction accuracy is clearly seen, with the accuracy reaching up to 97% in predicting compounds that can cause TdP and 90% for predicting compounds that do not cause TdP. The generic nature of the method has been demonstrated with four data sets available for carcinogenicity, where prediction accuracies were significantly higher, with a best receiver operating characteristics (ROC) value of 0.81 as against a best ROC value of 0.7 reported in the literature for the same data set. Thus, the method holds promise for wide applicability in toxicity prediction. |
| | |
Authors:
|
S Bhavani; A Nagargadde; A Thawani; V Sridhar; N Chandra |
Related Documents
:
|
16234338 - Improving the design and analysis of high-throughput screening technology comparison ex... 10582578 - Dissimilarity-based algorithms for selecting structurally diverse sets of compounds. 18966598 - Basic aspects and applications of tristimulus colorimetry. 14680988 - A functional observational battery for use in canine toxicity studies: development and ... 18700598 - Comparison of operation iraqi freedom and patient workload generator injury distributions. 18566968 - Cost-benefit analysis involving addictive goods: contingent valuation to estimate willi... |
Publication Detail:
|
Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
|
Title: Journal of chemical information and modeling Volume: 46 ISSN: 1549-9596 ISO Abbreviation: - Publication Date: 2006 Nov-Dec |
Date Detail:
|
Created Date: 2006-11-27 Completed Date: 2007-02-15 Revised Date: - |
Medline Journal Info:
|
Nlm Unique ID: 101230060 Medline TA: J Chem Inf Model Country: United States |
Other Details:
|
Languages: eng Pagination: 2478-86 Citation Subset: IM |
Affiliation:
|
Applied Research Group, Satyam Computer Services Limited, SID Block, IISc Campus, Bangalore, India. |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
Algorithms Carcinogens Chemistry, Pharmaceutical / methods* Computational Biology Drug Evaluation, Preclinical / instrumentation, methods* Drug Industry / instrumentation* Humans Models, Chemical Models, Statistical Neural Networks (Computer) Pattern Recognition, Automated* ROC Curve Sequence Analysis, Protein Software Torsades de Pointes / chemically induced* |
| Chemical | |
Reg. No./Substance:
|
0/Carcinogens |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: Fuzzy tricentric pharmacophore fingerprints. 1. Topological fuzzy pharmacophore triplets and adapted...
Next Document: New global communication process in thermodynamics: impact on quality of published experimental data...