Structure-based 3D-QSAR—merging the accuracy of structure-based alignments with the computational efficiency of ligand-based methods

Abstract

One of the major challenges in computational approaches to drug design is the accurate prediction of binding affinity of biomolecules. The strategies that can be applied for this purpose fall into two major categories—the indirect ligand-based and the direct receptor-based approach. In this contribution, we used a combination of both approaches in order to improve the prediction accuracy for drug molecules. The combined approach was tested on two sets of ligands for which the three-dimensional structure of the target receptor was known—estrogen receptor ligands and acetylcholinesterase inhibitors. The binding modes of the ligands under study were determined using an automated docking program ( AutoDock) and were compared with available X-ray structures of corresponding protein–ligand complexes. The ligand alignments obtained from the docking simulations were subsequently taken as the basis for a comparative field analysis applying the grid/golpe program. Using the interaction field derived with a water probe and applying the smart region definition variable selection, highly predictive models were obtained. The comparison of our models with interaction energy-based models and with traditional CoMFA models obtained using a ligand-based alignment indicates that the combination of structure-based and 3D-QSAR methods is able to improve the prediction ability of the underlying model.