A classification study of human β 3-adrenergic receptor agonists using BCUT descriptors

Binding Constants of Substituted Benzoic Acids with Bovine Serum Albumin

Experimental data on the affinity of various substances to albumin are essential for the development of empirical models to predict plasma binding of drug candidates. Binding of 24 substituted benzoic acid anions to bovine serum albumin was studied using spectrofluorimetric titration. The equilibrium constants of binding at 298 K were determined according to 1:1 complex formation model. The relationships between the ligand structure and albumin affinity are analyzed. The binding constant values for m- and p-monosubstituted acids show a good correlation with the Hammett constants of substituents. Two- and three-parameter quantitative structure–activity relationship (QSAR) models with theoretical molecular descriptors are able to satisfactorily describe the obtained values for the whole set of acids. It is shown that the electron-density distribution in the aromatic ring exerts crucial influence on the albumin affinity.

Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery

As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM.

A new chemoinformatics approach with improved strategies for effective predictions of potential drugs

Background

Fast and accurate identification of potential drug candidates against therapeutic targets (i.e., drug–target interactions, DTIs) is a fundamental step in the early drug discovery process. However, experimental determination of DTIs is time-consuming and costly, especially for testing the associations between the entire chemical and genomic spaces. Therefore, computationally efficient algorithms with accurate predictions are required to achieve such a challenging task. In this work, we design a new chemoinformatics approach derived from neighbor-based collaborative filtering (NBCF) to infer potential drug candidates for targets of interest. One of the fundamental steps of NBCF in the application of DTI predictions is to accurately measure the similarity between drugs solely based on the DTI profiles of known knowledge. However, commonly used similarity calculation methods such as COSINE may be noise-prone due to the extremely sparse property of the DTI bipartite network, which decreases the model performance of NBCF. We herein propose three strategies to remedy such a dilemma, which include: (1) adopting a positive pointwise mutual information (PPMI)-based similarity metric, which is noise-immune to some extent; (2) performing low-rank approximation of the original prediction scores; (3) incorporating auxiliary (complementary) information to produce the final predictions.

Results

We test the proposed methods in three benchmark datasets and the results indicate that our strategies are helpful to improve the NBCF performance for DTI predictions. Comparing to the prior algorithm, our methods exhibit better results assessed by a recall-based evaluation metric.

Conclusions

A new chemoinformatics approach with improved strategies was successfully developed to predict potential DTIs. Among them, the model based on the sparsity resistant PPMI similarity metric exhibits the best performance, which may be helpful to researchers for identifying potential drugs against therapeutic targets of interest, and can also be applied to related research such as identifying candidate disease genes.

Structural features of falcipain-3 inhibitors: an in silico study

Falcipain-3, the major cysteine hemoglobinase from the human malaria parasite Plasmodium falciparum, is critical for parasite development and is considered as a promising chemotherapeutic target. In order to understand the structure-activity correlation of falcipain-3 inhibitors, a set of ligand- and receptor-based 3D-QSAR models were developed in the present work employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for 247 2-pyrimidinecarbonitrile derivatives. An optimum ligand-based CoMSIA model yielded a cross validation Q(2) = 0.501, non-cross validation Rncv(2) = 0.821 and predictive Rpred(2) = 0.750. In addition, docking analysis and molecular dynamics simulation were applied to elucidate the probable binding modes of the ligand in the falcipain-3 binding pocket. Graphic representation of the results, as contoured 3D coefficient plots, also provides a clue to the reasonable modification of molecules. (1) Bulky substituents at the 3-position, and rings B and D increase the biological activity; (2) electrostatic groups at rings B, C and D are likely helpful to increase the falcipain-3 inhibition; (3) hydrophobic groups at rings B and D are favored; (4) Gly92, Ile94 and Thr95 which formed several H-bonds and a water-bridged H-bond are crucial for falcipain-3 inhibitors. This model, we hope, will be of help in designing and predicting novel falcipain-3 inhibitors.

Using self-organizing map (SOM) and support vector machine (SVM) for classification of selectivity of ACAT inhibitors

Using a self-organizing map (SOM) and support vector machine, two classification models were built to predict whether a compound is a selective inhibitor toward the two Acyl-coenzyme A: cholesterol acyltransferase (ACAT) isozymes, ACAT-1 and ACAT-2. A dataset of 97 ACAT inhibitors was collected. For each molecule, the global descriptors, 2D and 3D property autocorrelation descriptors and autocorrelation of surface properties were calculated from the program ADRIANA.Code. The prediction accuracies of the models (based on the training/ test set splitting by SOM method) for the test sets are 88.9 % for SOM1, 92.6 % for SVM1 model. In addition, the extended connectivity fingerprints (ECFP_4) for all the molecules were calculated and the structure-activity relationship of selective ACAT inhibitors was summarized, which may help find important structural features of inhibitors relating to the selectivity of ACAT isozymes.