Distributed Machine Learning Algorithms to classify Protein secondary structures for Drug Design-A Survey

Abstract

The arena of secondary structure based drug design is an evolving area in recent years. Structures help us to understand how the protein functions, and will help to design chemicals to be used pharmaceutically as modifiers of enzyme activity. X-Ray structures usually provide us with a quite static picture of the protein which is considered as expensive and time consuming. The recent modern drug design and development using knowledge of proteomics must rely on computational intelligence based machine learning model structures using efficient structure prediction techniques. In recent years, Machine learning, emerging on the basis of parallel and distributed computing for handling big data, is making huge advances in many areas. In this paper, we have captured a comprehensive review of protein structure prediction methods using Machine Learning approach in Distributed environment. Overall, the results are good and depicts that the accuracy and performance of protein secondary structure prediction methods are achieved using Machine Learning techniques and these would become a powerful aid, while implemented in Distributed environment like Hadoop or Spark. This research will be helpful for the recent medicine researchers, which aids in understanding the relation between protein sequences, structure and thereby determine the function to develop various drugs and designing novel enzymes and this is considered as one of the major focused areas in recent bioinformatics research.