Quantitative structure-activity relationship analysis of β-amyloid aggregation inhibitors

Inhibiting the aggregation process of the β-amyloid peptide is a promising strategy in treating Alzheimer’s disease. In this work, we have collected a dataset of 80 small molecules with known inhibition levels and utilized them to develop two comprehensive quantitative structure–activity relationship models: a Bayesian model and a decision tree model. These models have exhibited high predictive accuracy: 87% of the training and test sets using the Bayesian model and 89 and 93% of the training and test sets, respectively, by the decision tree model. Subsequently these models were used to predict the activities of several new potential β-amyloid aggregation inhibitors and these predictions were indeed validated by in vitro experiments. Key chemical features correlated with the inhibition ability were identified. These include the electro-topological state of carbonyl groups, AlogP and the number of hydrogen bond donor groups. The results demonstrate the feasibility of the developed models as tools for rapid screening, which could help in the design of novel potential drug candidates for Alzheimer’s disease.     

Visual exploration of structure–activity relationship using maximum common framework

To help tracking all molecules made in a typical medicinal chemistry project, we have developed an algorithm to generate a maximum common framework (MCF) hierarchy and an interactive tool for its visualization and analysis. By identifying all unique frameworks for a set of molecules and all molecules containing each framework, we were able to simplify the MCF hierarchy build up steps and, as a result, speed up the entire process significantly. By allowing compounds to be assigned to multiple MCFs, users can easily remove bad branching nodes and concentrate on interesting ones. MCF hierarchies provide an effective and intuitive visualization for tracking medicinal chemistry lead optimization projects. We will provide examples to illustrate its usefulness.     

A categorical structure–activity relationship analysis of GPR119 ligands

The categorical structure–activity relationship (cat-SAR) expert system has been successfully used in the analysis of chemical compounds that cause toxicity. Herein we describe the use of this fragment-based approach to model ligands for the G protein-coupled receptor 119 (GPR119). Using compounds that are known GPR119 agonists and compounds that we have confirmed experimentally that are not GPR119 agonists, four distinct cat-SAR models were developed. Using a leave-one-out validation routine, the best GPR119 model had an overall concordance of 99%, a sensitivity of 99%, and a specificity of 100%. Our findings from the in-depth fragment analysis of several known GPR119 agonists were consistent with previously reported GPR119 structure–activity relationship (SAR) analyses. Overall, while our results indicate that we have developed a highly predictive cat-SAR model that can be potentially used to rapidly screen for prospective GPR119 ligands, the applicability domain must be taken into consideration. Moreover, our study demonstrates for the first time that the cat-SAR expert system can be used to model G protein-coupled receptor ligands, many of which are important therapeutic agents.     

Development of a pharmacophore for cruzain using oxadiazoles as virtual molecular probes: quantitative structure–activity relationship studies

Chagas’s is a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi. According to the World Health Organization, 7 million people are infected worldwide leading to 7000 deaths per year. Drugs available, nifurtimox and benzimidazole, are limited due to low efficacy and high toxicity. As a validated target, cruzain represents a major front in drug discovery attempts for Chagas disease. Herein, we describe the development of 2D QSAR (\(r_{{{\text{pred}}}}^{2}\)?=?0.81) and a 3D-QSAR-based pharmacophore (\(r_{{{\text{pred}}}}^{2}\)?=?0.82) from a series of non-covalent cruzain inhibitors represented mostly by oxadiazoles (lead compound, IC₅₀?=?200 nM). Both models allowed us to map key intermolecular interactions in S1′, S2 and S3 cruzain sub-sites (including halogen bond and C?H/π). To probe the predictive capacity of obtained models, inhibitors available in the literature from different classes displaying a range of scaffolds were evaluate achieving mean absolute deviation of 0.33 and 0.51 for 2D and 3D models, respectively. CoMFA revealed an unexplored region where addition of bulky substituents to produce new compounds in the series could be beneficial to improve biological activity.