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.
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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: Morphine, an alkaloid isolated from the opium poppy, has been widely used as an analgesic, and has been a fascinating synthetic target of organic chemists. After the first total synthesis reported in 1952, a number of synthetic studies toward morphine have been reported, and findings obtained in such studies have greatly contributed to the progress of synthetic organic chemistry as well as medicinal chemistry. This review provides an overview of recent studies toward the total synthesis of morphine and related alkaloids. Work reported in the literature since 2004 will be reviewed.Topics in current chemistry 01/2011; 299:1-28. DOI:10.1007/128_2010_73 · 4.61 Impact Factor