Turbo Analytics: Applications of Big Data and HPC in Drug Discovery

Abstract

In this current age of data-driven science, perceptive research is being carried out in the areas of genomics, network and metabolic biology, human, animal, organ and tissue models of drug toxicity, witnessing or capturing key biological events or interactions for drug discovery. Drug designing and repurposing involves understanding of ligand orientations for proper binding to the target molecules. The crucial requirement of finding right pose of small molecule in ligand–protein complex is done using drug docking and simulation methods. The domains of biology like genomics, biomolecular structure dynamics, and drug discovery are capable of generating vast molecular data in range of terabytes to petabytes. The analysis and visualization of this data pose a great challenge to the researchers and needs to be addressed in an accelerated and efficient way. So there is continuous need to have advanced analytics platform and algorithms which can perform analysis of this data in a faster way. Big data technologies may help to provide solutions for these problems of molecular docking and simulations.