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J Chem Inf Model. 2013 Jan 28;53(1):114-22. doi: 10.1021/ci300508m. Epub 2013 Jan 9.

Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening.

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Department of Chemistry and Biochemistry, UCSD, La Jolla, California 92093-0359, USA.


Accurately ranking docking poses remains a great challenge in computer-aided drug design. In this study, we present an integrated approach called MIEC-SVM that combines structure modeling and statistical learning to characterize protein-ligand binding based on the complex structure generated from docking. Using the HIV-1 protease as a model system, we showed that MIEC-SVM can successfully rank the docking poses and consistently outperformed the state-of-art scoring functions when the true positives only account for 1% or 0.5% of all the compounds under consideration. More excitingly, we found that MIEC-SVM can achieve a significant enrichment in virtual screening even when trained on a set of known inhibitors as small as 50, especially when enhanced by a model average approach. Given these features of MIEC-SVM, we believe it provides a powerful tool for searching for and designing new drugs.

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