Antidiabetic Properties of Azardiracta indica and Bougainvillea spectabilis: In Vivo Studies in Murine Diabetes Model

Diabetes mellitus is a metabolic syndrome characterized by an increase in the blood glucose level. Treatment of diabetes is complicated due to multifactorial nature of the disease. Azadirachta indica Adr. Juss and Bougainvillea spectabilis are reported to have medicinal values including antidiabetic properties. In the present study using invivo diabetic murine model, A. indica and B. spectabilis chloroform, methanolic and aqueous extracts were investigated for the biochemical parameters important for controlling diabetes. It was found that A. indica chloroform extract and B. spectabilis aqueous, methanolic extracts showed a good oral glucose tolerance and significantly reduced the intestinal glucosidase activity. Interestingly, A. indica chloroform and B. spectabilis aqueous extracts showed significant increase in glucose-6-phosphate dehydrogenase activity and hepatic, skeletal muscle glycogen content after 21 days of treatment. In immunohistochemical analysis, we observed a regeneration of insulin-producing cells and corresponding increase in the plasma insulin and c-peptide levels with the treatment of A. indica chloroform and B. spectabilis aqueous, methanolic extracts. Analyzing the results, it is clear that A. indica chloroform and B. spectabilis aqueous extracts are good candidates for developing new neutraceuticals treatment for diabetes

BCL::Conf – Improved Open-Source Knowledge-Based Conformation Sampling using the Crystallographic Open Database

This paper describes recent improvements made to the BCL::Conf rotamer generation algorithm and comparison of its performance against other freely available and commercial conformer generation software. We demonstrate that BCL::Conf, with the use of rotamers derived from the COD, more effectively recovers crystallographic ligand-binding conformations seen in the PDB than other commercial and freely available software. BCL::Conf is now distributed with the COD-derived rotamer library, free for academic use. The BCL can be downloaded at http://meilerlab.org/ bclcommons for Windows, Linux, or Apple operating systems.</div

Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles

The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure–activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model’s performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23

MOESM1 of BCL::Conf: small molecule conformational sampling using a knowledge based rotamer library

Additional file 1. Supplementary data and protocol capture describing steps to reproduce data

Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery

The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/ property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines

Drugit: Crowd-sourcing molecular design of non-peptidic VHL binders

Given the role of human intuition in current drug design efforts, crowd-sourced \u27citizen scientist\u27 games have the potential to greatly expand the pool of potential drug designers. Here, we introduce ‘Drugit\u27, the small molecule design mode of the online ‘citizen science’ game Foldit. We demonstrate its utility for design with a use case to identify novel binders to the von Hippel Lindau E3 ligase. Several thousand molecule suggestions were obtained from players in a series of 10 puzzle rounds. The proposed molecules were then evaluated by in silico methods and by an expert panel and selected candidates were synthesized and tested. One of these molecules, designed by a player, showed dose-dependent shift perturbations in protein-observed NMR experiments. The co-crystal structure in complex with the E3 ligase revealed that the observed binding mode matched in major parts the player’s original idea. The completion of one full design cycle is a proof of concept for the Drugit approach and highlights the potential of involving citizen scientists in early drug discovery