Development and Validation of a Pharmacophore‐Based QSAR Model for the Prediction of CNS Activity

Abstract

A QSAR model aimed at predicting central nervous system (CNS) activity was developed based on the structure-activity relationships of compounds from an in-house database of "drug-like" molecules. These compounds were initially identified as "CNS-active" or "CNS-inactive", and pharmacophore 3D descriptors were calculated from multiple conformations for each structure. A linear discriminant analysis (LDA) was performed on this structure-activity matrix, and this QSAR model was able to correctly classify approximately 80 % in both a learning set and a validation set. For validation purposes, the LDA model was applied to compounds for which the blood-brain barrier (BBB) penetration was known, and all of them were correctly predicted. The model was also applied to 960 other in-house compounds for which in vitro binding tests were performed on 20 receptors known to be present at the CNS level, and a high correlation was observed between compounds predicted as CNS-active and experimental hits. Finally, the model was also applied to a set of 700 structures with known CNS activity or inactivity randomly chosen from public sources, and more than 70 % of the compounds were correctly classified, including novel CNS chemotypes. These results demonstrate the applicability of the model to novel chemical structures and its usefulness for designing original CNS-focused compound libraries.