Development and Validation of a Pharmacophore‐Based QSAR Model for the Prediction of CNS Activity
ABSTRACT A QSAR model for the prediction of CNS activity was developed and validated based on data from an in-house database of “drug-like” compounds. The model has demonstrated its applicability for novel chemical structures and its usefulness for the design of CNS-focused compound libraries.
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.
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ABSTRACT: Bioinformatics concerns itself with developing data-driven tools for extracting biological information from high-throughput datasets (e.g. gene expression data, protein interaction data). Until recently, most of these tools focus on analyzing a single source of data. However, all high-throughput measurements are noisy and incomplete. Therefore, we can improve the accuracy of our data driven predictions by combining various high-throughput measurements. In this thesis, we take a couple of steps towards data integration, which are reported in this thesis. We compare and introduce several computational methods for integrating different measurements.
- Value in Health 11/2008; 11(6). DOI:10.1016/S1098-3015(10)66307-3 · 2.89 Impact Factor
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ABSTRACT: This chapter contains sections titled: IntroductionKey Computer Methods for ADME/Tox PredictionsPreparation of Compound Collections and Computer Programs, Challenging ADME/Tox Predictions and Statistical MethodsADME/Tox Predictions within Pharmaceutics CompaniesChallenging ADME/Tox PredictionsStatistical Methods Conclusions ReferencesADMET for Medicinal Chemists, 11/2010: pages 29-124; , ISBN: 9780470484074