Full metadata record
DC FieldValueLanguage
dc.contributor.authorYang, JMen_US
dc.contributor.authorChen, YFen_US
dc.contributor.authorShen, TWen_US
dc.contributor.authorKristal, BSen_US
dc.contributor.authorHsu, DFen_US
dc.date.accessioned2014-12-08T15:18:48Z-
dc.date.available2014-12-08T15:18:48Z-
dc.date.issued2005-07-01en_US
dc.identifier.issn1549-9596en_US
dc.identifier.urihttp://dx.doi.org/10.1021/ci050034wen_US
dc.identifier.urihttp://hdl.handle.net/11536/13517-
dc.description.abstractVirtual screening of molecular compound libraries is a potentially powerful and inexpensive method for the discovery of novel lead compounds for drug development. The major weakness of virtual screening-the inability to consistently identify true positives (leads)-is likely due to our incomplete understanding of the chemistry involved in ligand binding and the subsequently imprecise scoring algorithms. It has been demonstrated that combining multiple scoring functions (consensus scoring) improves the enrichment of true positives. Previous efforts at consensus scoring have largely focused on empirical results, but they have yet to provide a theoretical analysis that gives insight into real features of combinations and data fusion for virtual screening. Results: We demonstrate that combining multiple scoring functions improves the enrichment of true positives only if (a) each of the individual scoring functions has relatively high performance and (b) the individual scoring functions are distinctive. Notably, these two prediction variables are previously established criteria for the performance of data fusion approaches using either rank or score combinations. This work, thus, establishes a potential theoretical basis for the probable success of data fusion approaches to improve yields in in silico screening experiments. Furthermore, it is similarly established that the second criterion (b) can, in at least some cases, be functionally defined as the area between the rank versus score plots generated by the two (or more) algorithms. Because rank-score plots are independent of the performance of the individual scoring function, this establishes a second theoretically defined approach to determining the likely success of combining data from different predictive algorithms. This approach is, thus, useful in practical settings in the virtual screening process when the performance of at least two individual scoring functions (such as in criterion a) can be estimated as having a high likelihood of having high performance, even if no training sets are available. We provide initial validation of this theoretical approach using data from five scoring systems with two evolutionary docking algorithms on four targets, thymidine kinase, human dihydrofolate reductase, and estrogen receptors of antagonists and agonists. Our procedure is computationally efficient, able to adapt to different situations, and scalable to a large number of compounds as well as to a greater number of combinations. Results of the experiment show a fairly significant improvement (vs single algorithms) in several measures of scoring quality, specifically "goodness-of-hit" scores, false positive rates, and "enrichment". This approach (available online at http://gemdock.life. nctu.edu.tw/dock/download.php) has practical utility for cases where the basic tools are known or believed to be generally applicable, but where specific training sets are absent.en_US
dc.language.isoen_USen_US
dc.titleConsensus scoring criteria for improving enrichment in virtual screeningen_US
dc.typeArticleen_US
dc.identifier.doi10.1021/ci050034wen_US
dc.identifier.journalJOURNAL OF CHEMICAL INFORMATION AND MODELINGen_US
dc.citation.volume45en_US
dc.citation.issue4en_US
dc.citation.spage1134en_US
dc.citation.epage1146en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000230864300035-
dc.citation.woscount112-
Appears in Collections:Articles


Files in This Item:

  1. 000230864300035.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.