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A consensus partial least squares (PLS)-similarity based k-nearest neighbors (KNN) model utilizing 3D-SDAR (three dimensional spectral data-activity relationship) fingerprint descriptors for prediction of the log(1\/EC<jats:sub>50<\/jats:sub>) values of a dataset of 94 aryl hydrocarbon receptor binders was developed. This consensus model was constructed from a PLS model utilizing<jats:italic>10\u00a0ppm x 10\u00a0ppm x 0.5\u00a0\u00c5<\/jats:italic>bins and 7 latent variables (R<jats:sup>2<\/jats:sup><jats:sub>test<\/jats:sub>of 0.617), and a KNN model using<jats:italic>2\u00a0ppm x 2\u00a0ppm x 0.5\u00a0\u00c5<\/jats:italic>bins and 6 neighbors (R<jats:sup>2<\/jats:sup><jats:sub>test<\/jats:sub>of 0.622). Compared to individual models, improvement in predictive performance of approximately 10.5% (R<jats:sup>2<\/jats:sup><jats:sub>test<\/jats:sub>of 0.685) was observed. Further experiments indicated that this improvement is likely an outcome of the complementarity of the information contained in 3D-SDAR matrices of different granularity. For similarly sized data sets of Aryl hydrocarbon (AhR) binders the consensus KNN and PLS models compare favorably to earlier reports. The ability of 3D-QSDAR (three dimensional quantitative spectral data-activity relationship) to provide structural interpretation was illustrated by a projection of the most frequently occurring bins on the standard coordinate space, thus allowing identification of structural features related to toxicity.<\/jats:p>","DOI":"10.1186\/1758-2946-5-47","type":"journal-article","created":{"date-parts":[[2013,11,21]],"date-time":"2013-11-21T08:02:35Z","timestamp":1385020955000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Complementary PLS and KNN algorithms for improved 3D-QSDAR consensus modeling of AhR binding"],"prefix":"10.1186","volume":"5","author":[{"given":"Svetoslav H","family":"Slavov","sequence":"first","affiliation":[]},{"given":"Bruce A","family":"Pearce","sequence":"additional","affiliation":[]},{"given":"Dan A","family":"Buzatu","sequence":"additional","affiliation":[]},{"given":"Jon G","family":"Wilkes","sequence":"additional","affiliation":[]},{"given":"Richard D","family":"Beger","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2013,11,21]]},"reference":[{"key":"485_CR1","doi-asserted-by":"publisher","first-page":"2110","DOI":"10.1021\/ci050529l","volume":"46","author":"M Ganguly","year":"2006","unstructured":"Ganguly M, Brown N, Schuffenhauer A, Ertl P, Gillet VJ, Greenidge PA: Introducing the Consensus Modeling Concept in Genetic Algorithms: Application to Interpretable Discriminant Analysis. 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