Exploring Artificial Intelligence in Drug Discovery: A Comprehensive Review

Drug discovery and development process is very lengthy, highly expensive and extremely complex in nature. Traditional methods involve expensive techniques and take many years to bring a new drug to the market. With the advent of new tools and technologies in this field, the major challenge is to reduce the time and cost required for the development of a new drug. These complex problems involve extremely high computations and can be addressed with the help of Artificial Intelligence based techniques. In this paper, we have broadly discussed different emerging applications of artificial intelligence in the field of drug discovery and development including identification of gene targets for diseases, repurposing of existing drugs through pathway networks, improvements in structure modelling, virtual screenings and hit identification, ADMET prediction, lead identification, clinical trials etc. using various artificial intelligence methods and their inter comparisons. This review presents the literature survey of different research articles published in reputed journals of international publishers such as Springer, Science Direct, IEEE Xplore, Elsevier etc. This is a systematic review of 143 publications to provide an organized summary. In addition to the in-depth analysis the foreseen challenges and existing limitations associated with drug discovery and development process are also pointed out in bold and humble suggestions have been made for necessary improvements. Readers, who are new to the field, will find it useful for enhancing their view about the field.