Benchmarking of QSAR models for blood-brain barrier permeation

Konovalov, Dmitry A., Coomans, Danny, Deconinck, Eric, and Vander Heyden, Yvan (2007) Benchmarking of QSAR models for blood-brain barrier permeation. Journal of Chemical Information and Modeling, 47 (4). pp. 1648-1656.

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View at Publisher Website: http://dx.doi.org/10.1021/ci700100f

Abstract

Using the largest available database of 328 blood-brain distribution (logBB) values, a quantitative benchmark was proposed to allow for a consistent comparison of the predictive accuracy of current and future logBB/ quantitative structure-activity relationship (-QSAR) models. The usefulness of the benchmark was illustrated by comparing the global and k-nearest neighbors (kNN) multiple-linear regression (MLR) models based on the linear free-energy relationship (LFER) descriptors, and one non-LFER-based MLR model. The leaveone-out (LOO) and leave-group-out Monte Carlo (MC) cross-validation results (q2 ) 0.766, qms ) 0.290, and qmsmc ) 0.311) indicated that the LFER-based kNN-MLR model was currently one of the most accurate predictive logBB-QSAR models. The LOO, MC, and kNN-MLR methods have been implemented in the QSAR-BENCH program, which is freely available from www.dmitrykonovalov.org for academic use.

Item ID: 8716
Item Type: Article (Refereed Research - C1)
ISSN: 1549-960X
Date Deposited: 02 Mar 2010 03:15
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
Citation Count from Web of Science Web of Science 30
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