The artificial neural network selects saccharides from natural sources a promise for potential FimH inhibitor to prevent UTI infections.

Unlabelled: The major challenge in the development of affordable medicines from natural sources is the unavailability of logical protocols to explain their mechanism of action in biological targets. FimH (Type 1 fimbrin with D-mannose specific adhesion property), a lectin on E. coli cell surface is a promising target to combat the urinary tract infection (UTI).

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

Background: Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis (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.