QSAR Analysis of Indazole Estrogens as Selective β-Estrogen Receptor Ligands: Rationalization of Physicochemical Properties

Predicting network of drug-enzyme interaction based on machine learning method

It is important to correctly and efficiently map drugs and enzymes to their possible interaction network in modern drug research. In this work, a novel approach was introduced to encode drug and enzyme molecules with physicochemical molecular descriptors and pseudo amino acid composition, respectively. Based on this encoding method, Random Forest was adopted to build the drug-enzyme interaction network. After selecting the optimal features that are able to represent the main factors of drug-enzyme interaction in our prediction, a total of 129 features were attained which can be clustered into nine categories: Elemental Analysis, Geometry, Chemistry, Amino Acid Composition, Secondary Structure, Polarity, Molecular Volume, Codon Diversity and Electrostatic Charge. It is further found that Geometry features were the most important of all the features. As a result, our predicting model achieved an MCC of 0.915 and a sensitivity of 87.9% at the specificity level of 99.8% for 10-fold cross-validation test, and achieved an MCC of 0.895 and a sensitivity of 95.7% at the specificity level of 95.4% for independent set test. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

New descriptors of amino acids and their application to peptide QSAR study

A new set of descriptors was derived from a matrix of three structural variables of the natural amino acid, including van der Waal's volume, net charge index and hydrophobic parameter of side residues. They were selected from many properties of amino acid residues, which have been validated being the key factors to influence the interaction between peptides and its protein receptor. They were then applied to structure characterization and QSAR analysis on bitter tasting di-peptide, agiotensin-converting enzyme inhibitor and bactericidal peptides by using multiple linear regression (MLR) method. The leave one out cross validation values (Q(2)) were 0.921, 0.943 and 0.773. The multiple correlation coefficients (R(2)) were 0.948, 0.970 and 0.926, the root mean square (RMS) error for estimated error were 0.165, 0.154 and 0.41, respectively for bitter tasting di-peptide, angiotensin-converting enzyme inhibitor and bactericidal peptides. Test sets of peptides were used to validate the quantitative model, and it was shown that all these QSAR models had good predictability for outside samples. The results showed that, in comparison with the conventional descriptors, the new set of descriptors is a useful structure characterization method for peptide QSAR analysis, which has multiple advantages, such as definite physical and chemical meaning, easy to get, and good structural characterization ability.

[Novel access to indazoles based on palladium-catalyzed amination chemistry]

Two efficient methods to construct the indazole nucleus have been developed, both of which utilize palladium-catalyzed intramolecular carbon-nitrogen bond formation. One is based on intramolecular Buchwald-Hartwig amination reaction of 2-halobenzophenone tosylhydrazones. The catalyst system we developed for this reaction allows the cyclization to proceed under very mild conditions and thus could be applied to a wide range of substrates with acid- or base-sensitive functional groups. Furthermore, this methodology could be applied for the construction of benzoisoxazole ring system. In addition, catalytic C-H activation with palladium followed by intramolecular amination of benzophenone tosylhydrazones was also accomplished with the aid of the catalyst system such as Pd(OAc)(2)/Cu(OAc)(2)/AgOCOCF(3), which gave another route to indazoles. Using this combination, indazoles with various functional groups could be obtained in good to high yields, especially in the case of substrates having electron donating group such as methoxy group on benzene ring. Interesting chemo- and regioselectivity were also observed in this reaction.