Review of Machine Learning Approach for Drug Development Process
Devottam Gaurav, Fernando Ortiz, Sanju Tiwari, M.A. Jabbar
Abstract
The availability of clinical and natural information is present in large abundance. To handle or study the large quantity of drugs for development and testing, manual power has a large effort as well as a large amount of time is being consumed. So, the extra effort and consumption of a large amount of time in the study of the development of drugs seem to be harder in the traditional approach. Hence, to make the development process of drugs more effective, an automated approach may be a solution to the low profitability rate that drug organizations presently face. The automated approach that is used to develop the drug may guide, or accelerate, the development process; give a superior comprehension of drug-related diseases; relate the biological aspects of the drug; help to arrange the preclinical laboratory tests, and the future clinical preliminaries. In this paper, our research work will primarily focus on scaling up the effectiveness of drug development process.