Tailored scoring function of Trypsin–benzamidine complex using COMBINE descriptors and support vector regression

Structure-based drug design (SBDD) is a computational technique for designing new drug candidates based on physico-chemical interactions between a protein and a ligand molecule. The most important thing for SBDD is accurate estimation of binding affinity of the ligand molecule against the target protein. Scoring function, which is basically a mathematical equation that approximates the thermodynamics of binding, has to be defined in advance. In this paper, we propose a novel method for building a tailored scoring function using comparative molecular binding energy (COMBINE) descriptors and support vector regression (SVR). COMBINE descriptors are energy terms between the ligand molecule and each amino acid residue of the target protein. SVR is a promising nonlinear regression method based on the theory of support vector machine (SVM). In these types of regression methodology, variable selection is one of the most important issues to construct a robust and predictive quantitative structure–activity relationship (QSAR) model. We adopted a variable selection method based on sensitivity analysis of each variable. The usefulness of the proposed method has been validated by applying to real QSAR data set, benzamidine derivatives as Trypsin inhibitors. The final SVR model could successfully identify important amino acid residues for explaining inhibitory activities.