This review is to summarize three new QSAR (quantitative structure-activity relationship) methods recently developed in our group and their applications for drug design. Based on more solid theoretical models and advanced mathematical techniques, the conventional QSAR technique has been recast in the following three aspects. (1) In the fragment-based two dimensional QSAR, or abbreviated as FB-QSAR, the molecular structures in a family of drug candidates are divided into several fragments according to the substitutes being investigated. The bioactivities of drug candidates are correlated with physicochemical properties of the molecular fragments through two sets of coefficients: one is for the physicochemical properties and the other for the molecular fragments. (2) In the multiple field three dimensional QSAR, or MF-3D-QSAR, more molecular potential fields are integrated into the comparative molecular field analysis (CoMFA) through two sets of coefficients: one is for the potential fields and the other for the Cartesian three dimensional grid points. (3) In the AABPP (amino acid-based peptide prediction), the bioactivities of peptides or proteins are correlated with the physicochemical properties of all or partial residues of the sequence through two sets of coefficients: one is for the physicochemical properties of amino acids and the other for the weight factors of the residues. Meanwhile, an iterative double least square (IDLS) technique is developed for solving the two sets of coefficients in a training dataset alternately and iteratively. Using the two sets of coefficients, one can predict the bioactivity of a query peptide, protein, or drug candidate. Compared with the old methods, the new QSAR approaches as summarized in this review possess machine learning ability, can remarkably enhance the prediction power, and provide more structural information. Meanwhile, the future challenge and possible development in this area have been briefly addressed as well.

In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus.

Cation-π interaction is comparable and as important as other main molecular interaction types, such as hydrogen bond, electrostatic interaction, van der Waals interaction, and hydrophobic interaction. Cation-π interactions frequently occur in protein structures, because six (Phe, Tyr, Trp, Arg, Lys, and His) of 20 natural amino acids and all metallic cations could be involved in cation-π interaction. Cation-π interactions arise from complex physicochemical nature and possess unique interaction behaviors, which cannot be modeled and evaluated by existing empirical equations and force field parameters that are widely used in the molecular dynamics. In this study, the authors present an empirical approach for cation-π interaction energy calculations in protein interactions. The accurate cation-π interaction energies of aromatic amino acids (Phe, Tyr, and Try) with protonated amino acids (Arg and Lys) and metallic cations (Li(+), Na(+), K(+), and Ca(2+)) are calculated using B3LYP/6-311+G(d,p) method as the benchmark for the empirical formulization and parameterization. Then, the empirical equations are built and the parameters are optimized based on the benchmark calculations. The cation-π interactions are distance and orientation dependent. Correspondingly, the empirical equations of cation-π interactions are functions of two variables, the distance r and the orientation angle θ. Two types of empirical equations of cation-π interactions are proposed. One is a modified distance and orientation dependent Lennard-Jones equation. The second is a polynomial function of two variables r and θ. The amino acid-based empirical equations and parameters provide simple and useful tools for evaluations of cation-π interaction energies in protein interactions.

Due to the low toxicity, easy synthesis, rapid elimination, and less side effect, more and more peptide inhibitors are emerging as the effective drugs that are clinically used in therapies of a number of diseases. At the same time the computer-aided drug design (CADD) methods have remarkably developed. In this mini review the newly developed peptide inhibitors and drugs are introduced, including peptide vaccines for cancers, peptide inhibitors for HIV, Alzheimer's disease and related diseases, and the peptides as the leading compounds of drugs. The recent progress in the theory and methodology of peptide inhibitor design is reviewed. (1) The flexible protein-peptide docking model is introduced, in which the peptide structures are treated as segment-flexible chains using genetic algorithm and special force field parameters. (2) The "Wenxiang diagram" is illustrated for protein-peptide interaction analysis that has been successfully used in the coiled-coil interaction analysis. (3) The "Distorted key" theory is reviewed, which is an effective method to convert the peptide inhibitors to the small chemical drugs. (4) The amino acid property-based peptide prediction method (AABPP) is described that is a twolevel QSAR prediction network for the bioactivity prediction of peptide inhibitors. (5) Finally, several types of molecular interactions between protein and peptide ligands are summarized, including cation-π interactions; polar hydrogen-π interactions; and π-π stocking interactions.