Prediction of bloodbrain partitioning: A model based on molecular electronegativity distance vector descriptors
The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the bloodbrain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (Rp). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and Rp=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as CH3, CH2, CH, C , C, CH<, C<, N, NH, O, and OH, are the most important factors affecting the BBB permeability.
molecular electronegativity distance vector (medv) bloodbrain barrier (bbb) permeability plsr the variable importance in projection (vip) qsar