Computation of Binding Energies Including Their Enthalpy and Entropy Components for Protein-Ligand Complexes Using Support Vector Machines.

Computing binding energies of protein-ligand complexes including their enthalpy and entropy terms by means of computational methods is an appealing approach for selecting initial hits and for further optimization in early stages of drug discovery. Despite the importance, computational predictions of thermodynamic components have evaded attention and reasonable solutions. In this study, support vector machines are used for developing scoring functions to compute binding energies and their enthalpy and entropy components of protein-ligand complexes. The binding energies computed from our newly derived scoring functions have better Pearson's correlation coefficients with experimental data than previously reported scoring functions in benchmarks for protein-ligand complexes from the PDBBind database. The protein-ligand complexes with binding energies dominated by enthalpy or entropy term could be qualitatively classified by the newly derived scoring functions with high accuracy. Furthermore, it is found that the inclusion of comprehensive descriptors based on ligand properties in the scoring functions improved the accuracy of classification as well as the prediction of binding energies including their thermodynamic components. The prediction of binding energies including the enthalpy and entropy components using the support vector machine based scoring functions should be of value in the drug discovery process.