مقایسه فضایی pharmacophores و خوشه شباهت مولکولی به عنوان ابزار QSAR
Abstract: A training set of 55 antifungal P450 analogue inhibitors was used to RI (Receptor Independent) 4D-QSAR models. Ten different alignments were used to build the models, and one alignment yields a significantly better model than the other alignments. Two different methodologies were used to measure the similarity of the best 4D-QSAR models of each alignment. The highest 3D-pharmacophore similarity correlation coefficient between any pair of 4D-QSAR models from the 10 alignments considered is only 0.216. However, the best 4D-QSAR models of each alignment do contain some proximate common sites. The inferred active sites mapped out by the 4D-QSAR models suggest that hydrogen bond interactions are not prevalent when this class of P450 analogue inhibitors binds to the receptor active site. This feature of the 4D-QSAR models is in agreement with the crystal structure results that indicate no ligand-receptor hydrogen bonds are formed. A 115 compound dataset for HSA (Human Serum Albumin) binding is divided into the training set and the test set based on molecular similarity and cluster analyses. 2D-connectivty similarity measure and 4D-fingerprint similarity measure were applied to this data set. Four different predictive schemes (SM, SA, SR, SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM) predicts the binding affinity of a test compound using only the most similar training set compound\'s binding affinity. This scheme has relatively poor predictivity based both on 2D-connectivity similarity measures and 4D-fingerprints similarity analyses. The other three algorithmic schemes (SM SR, SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. Both the 2D-connectivity similarity measures and the 4D-fingerprints have nearly same predictivity for this particular dataset.
Keywords: Health and environmental sciences, Pharmacophores, Molecular similarity, Clustering, QSAR