Pharmacophore-similarity-based QSAR (PS-QSAR) for group-specific biological activity predictions
Sivakumar Prasanth Kumar, Yogesh T. Jasrai, Himanshu Pandya, Rakesh Rawal
Abstract
Sivakumar Prasanth Kumar, Yogesh T. Jasrai, Himanshu Pandya, Rakesh Rawal
Abstract
Recent technological breakthroughs in medicinal chemistry arena had ameliorated the perspectives of quantitative structure-activity relationship (QSAR) methods. In this direction, we developed a group-based QSAR method based on pharmacophore-similarity concept which takes into account the 2D topological pharmacophoric descriptors and predicts the group-specific biological activities. This activity prediction may assist the contribution of certain pharmacophore features encoded by respective fragments toward activity improvement and/or detrimental effects. We termed this method as pharmacophore-similarity-based QSAR (PS-QSAR) and studied the activity contribution of fragments from 3-hydroxypyridinones derivatives possessing antimalarial activities.
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Recent technological breakthroughs in medicinal chemistry arena had ameliorated the perspectives of quantitative structure-activity relationship (QSAR) methods. In this direction, we developed a group-based QSAR method based on pharmacophore-similarity concept which takes into account the 2D topological pharmacophoric descriptors and predicts the group-specific biological activities. This activity prediction may assist the contribution of certain pharmacophore features encoded by respective fragments toward activity improvement and/or detrimental effects. We termed this method as pharmacophore-similarity-based QSAR (PS-QSAR) and studied the activity contribution of fragments from 3-hydroxypyridinones derivatives possessing antimalarial activities.
Key concepts: Pharmacophore, Quantitative structure–activity relationship, Similarity (geometry), Chemistry, Group (periodic table), Molecular descriptor, Computational biology, Stereochemistry