Liu Jianzhong, Yang Liu, Li Yi, Pan Dahua, Hopfinger Anton J
Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, 19716, USA.
J Comput Aided Mol Des. 2005 Aug;19(8):567-83. doi: 10.1007/s10822-005-9012-4. Epub 2005 Nov 3.
A 115 compound dataset for HSA binding is divided into the training set and the test set based on molecular similarity and cluster analyses. Both Kier-Hall valence connectivity indices and 4D-fingerprint similarity measures were applied to this dataset. 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 Kier-Hall valence connectivity indices 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. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. This study supports that some types of similarity measures are highly similar to one another for this dataset. Both the Kier-Hall valence connectivity indices similarity measures and the 4D-fingerprints have nearly same predictivity for this particular dataset.
