Name
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 leave-one-out (LOO) and leave-group-out Monte Carlo (MC) cross-validation results (q(2) = 0.766, qms = 0.290, and qms(mc) = 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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1021/ci700100f | JOURNAL OF CHEMICAL INFORMATION AND MODELING |
Keywords | Field | DocType |
monte carlo,quantitative structure activity relationship,multiple linear regression,free energy,cross validation,k nearest neighbor | Data mining,Quantitative structure–activity relationship,Monte Carlo method,Taft equation,Regression,Computer science,Loo,Benchmarking | Journal |
Volume | Issue | ISSN |
47 | 4 | 1549-9596 |
Citations | PageRank | References |
5 | 0.45 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmitry A. Konovalov | 1 | 56 | 7.49 |
| Danny Coomans | 2 | 105 | 19.07 |
| Eric Deconinck | 3 | 18 | 2.21 |
| Yvan Vander Heyden | 4 | 16 | 1.90 |