Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

<p>Abstract</p> <p>Background</p> <p>Tuberculosis is a contagious disease caused by <it>Mycobacterium tuberculosis </it>(Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.</p> <p>Results</p> <p>We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.</p> <p>Conclusions</p> <p>This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.</p

The influence of stiffeners on axial crushing of glass-fabric-reinforced epoxy composite shells

A generic static and impact experimental procedure has been developed in this work aimed at improving the stability of glass fabric reinforced epoxy shell structures by bonding with axial stiffeners. Crashworthy structures fabricated from composite laminate with stiffeners would offer energy absorption superior to metallic structures under compressive loading situations. An experimental material characterisation of the glass fabric reinforced epoxy composite under uni-axial tension has been carried out in this study. This work provides a numerical simulation procedure to describe the static and dynamic response of unstiffened glass fabric reinforced epoxy composite shell (without stiffeners) and stiffened glass fabric reinforced epoxy composite shell (with axial stiffeners) under static and impact loading using the Finite Element Method. The finite element calculation for the present study was made with ANSYS®-LS-DYNA® software. Based upon the experimental and numerical investigations, it has been asserted that glass fabric reinforced epoxy shells stiffened with GFRP stiffeners are better than unstiffened glass fabric reinforced epoxy shell and glass fabric reinforced epoxy shell stiffened with aluminium stiffeners. The failure surfaces of the glass fabric reinforced epoxy composite shell structures tested under impact were examined by SEM

Computational prediction of binding affinity for CYP1A2-ligand complexes using empirical free energy calculations

Predicting binding affinities for receptor-ligand complexes is still one of the challenging processes in computational structurebased ligand design. Many computational methods have been developed to achieve this goal, such as docking and scoring methods, the linear interaction energy (LIE) method, and methods based on statistical mechanics. In the present investigation, we started from an LIE model to predict the binding free energy of structurally diverse compounds of cytochrome P450 1A2 ligands, one of the important human metabolizing isoforms of the cytochrome P450 family. The data set includes both substrates and inhibitors. It appears that the electrostatic contribution to the binding free energy becomes negligible in this particular protein and a simple empirical model was derived, based on a training set of eight compounds. The root mean square error for the training set was 3.7 kJ/mol. Subsequent application of the model to an external test set gives an error of 2.1 kJ/mol, which is remarkably good, considering the simplicity of the model. The structures of the proteinligand interactions are further analyzed, again demonstrating the large versatility and plasticity of the cytochrome P450 active site