Illustrious Implications of Nature-Inspired Computing Methods in Therapeutics and Computer-Aided Drug Design

Abstract

The Nature-inspired computing (NIC) techniques have been effectively applied to research pharmaceutical components and compounds. NIC includes problem-solving methods based on abstractions of natural processes and provides new ways to understand, model, and analyse natural complexity. These algorithms mimic biological systems to create new computation paradigms, such as swarm intelligence, neural networks, and evolutionary computing. Nowadays, the NIC algorithms are becoming very popular in solving complex optimisation in most academic and industrial fields, including drug design, development, therapeutics, molecular modelling, and peptide design. These algorithms work on a combinatorial approach for small molecules and compound designs that rely on the pharmacological properties of novel drug candidates. Over the last decade, NIIC techniques have been successfully applied in each drug discovery and development pipeline stage to overrule the obstacle of complex and big data from genomics, proteomics, microarray data, and clinical trials. This chapter summarised the recent applications of NIC methods in therapeutics and computer-aided drug design.