Nature-Inspired Computing Techniques in Drug Design, Development, and Therapeutics

Abstract

Drug design and development is a long process that consumes lots of time and money. The process divides into different stages, where the most important step is to evaluate the safety and efficacy of the drugs after finding the best lead compounds. Several in-vitro methods have been developed to evaluate the toxicity of the drugs during the preclinical screening stage; however, these assays are super expensive and costly. However, the safety assessment of the drugs is very important to develop a very accurate and precise therapeutic application. Therefore, it is needed to design new alternative methods such as computational methods for high throughput drug designing and development for very precise and effective therapeutic applications. Development of highly advanced nature-inspired intelligent computing (NIC) technologies such as particle swarm optimization (PSO), ant colony optimization (ACO), DNA computing connected with the artificial immune systems, and machine learning helps in accurate drug designing, big data processing, integration of big data for the development of prediction models, disease-based image processing to evaluate pre- and post-drug effects on biological systems, etc. In this chapter, we provide a deep insight into the usage of nature-inspired intelligent computing technologies in drug design, development, and therapeutics.