The original quantitative structure-activity relationship (QSAR) formulation was proposed by Hansch and Fujita in the 1960's and, since then QSAR analysis has evolved as a mature science, due mainly to the advances that occurred in the past two decades in the fields of molecular modeling, data analysis algorithms, chemoinformatics, and the application of graph theory in chemistry. Moreover, it is also worthy of note the exponential progress that have occurred in software and hardware development. In this context, a myriad of QSAR methods exist; from the considered "classical" approaches (known as two-dimensional (2D) QSAR), to three-dimensional (3D) and multidimensional (nD) QSAR ones. A distinct QSAR approach has been recently proposed, the receptor-dependent-QSAR, where explicit information regarding the receptor structure (usually a protein) is extensively used during modeling process. Indeed, a limited, but growing number of receptor-dependent QSAR methods are reported in the literature. With no intention to be comprehensive, an overview of receptor-dependent QSAR methods will be discussed along with an in-depth examination of their applications in drug design, virtual screen, and ADMET modeling in silico.

Abstract:Prediction of peptides binding to HLA (human leukocyte antigen) finds application in peptide vaccine design. A number of statistical and structural models have been developed in recent years for HLA binding peptide prediction. However, a Bayesian Network (BNT) model is not available. In this study we describe a BNT model for HLA-A2 binding peptide prediction. It has been demonstrated that the BNT model allows up to 99% accurate identification of the HLA-A2 binding peptides and provides similar prediction accuracy compared to HMM (Hidden Markov Model) and ANN (Artificial Neural Network). At the same time, it has been shown that the BNT has that advantage that it allows more accurate performance for smaller sets of empirical data compared to the HMM and the ANN methods. When the size of the training set has been reduced to 40% from the original data, the identification of the HLA-A2 binding peptides by the BNT, ANN and HMM methods produced ARoc (area under receiver operating characteristic) values 0.88, 0.85, 0.85 respectively. The results of the work demonstrate certain advantages of using the Bayesian Networks in predicting the HLA binding peptides using smaller datasets.

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