3 research outputs found
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
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