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Issue No.05 - Sept.-Oct. (2012 vol.9)
pp: 1301-1313
Hossam M. Ashtawy , Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
Nihar R. Mahapatra , Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
Accurately predicting the binding affinities of large sets of protein-ligand complexes efficiently is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology. Since a scoring function (SF) is used to score, rank, and identify drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein's binding site has a significant bearing on the accuracy of virtual screening. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited ranking accuracy has been a major roadblock toward cost-effective drug discovery. Therefore, in this work, we explore a range of novel SFs employing different machine-learning (ML) approaches in conjunction with a variety of physicochemical and geometrical features characterizing protein-ligand complexes. We assess the ranking accuracies of these new ML-based SFs as well as those of conventional SFs in the context of the 2007 and 2010 PDBbind benchmark data sets on both diverse and protein-family-specific test sets. We also investigate the influence of the size of the training data set and the type and number of features used on ranking accuracy. Within clusters of protein-ligand complexes with different ligands bound to the same target protein, we find that the best ML-based SF is able to rank the ligands correctly based on their experimentally determined binding affinities 62.5 percent of the time and identify the top binding ligand 78.1 percent of the time. For this SF, the Spearman correlation coefficient between ranks of ligands ordered by predicted and experimentally determined binding affinities is 0.771. Given the challenging nature of the ranking problem and that SFs are used to screen millions of ligands, this represents a significant improvement over the best conventional SF we studied, for which the corresponding ranking performance values are 57.8 percent, 73.4 percent, and 0.677.
proteins, biochemistry, biology computing, drugs, learning (artificial intelligence), molecular biophysics, Spearman correlation coefficient, comparative assessment, machine-learning-based scoring functions, protein-ligand binding affinity prediction, protein-ligand complexes, computational biomolecular science, drug discovery, physicochemical feature, geometrical feature, 2010 PDBbind benchmark data sets, protein-family-specific test sets, 2007 PDBbind benchmark data sets, training data set, Proteins, Feature extraction, Training, Databases, Drugs, Accuracy, Three dimensional displays, virtual screening., Drug discovery, machine learning, protein-ligand binding affinity, ranking power, scoring function
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