Exploring the Advances of AI in Drug Development.
TL;DR
This review discusses the applications of AI in diverse and prominent processes of drug discovery, like identification of a diseased state, identification of target, development of lead compounds, virtual screening, and drug toxicity.
Abstract
Drug discovery is a tedious process that takes a long time and incurs high costs in developing an approved drug for clinical use. The long time and high expenditure are due to various phases of drug discovery and do not guarantee the success of the drug for clinical use, and about 90% of potential drug candidates suffer failure in phase-I clinical trials. The drug molecule qualifying for phase I clinical trial after passing through the preclinical stages is a significant milestone for both research institutes and pharmaceutical companies. Therefore, there is a need to explore alternatives for the drug discovery process. In this regard, Artificial Intelligence may provide significant assistance in different processes of drug discovery. This review discusses the applications of AI in diverse and prominent processes of drug discovery, like identification of a diseased state, identification of target, development of lead compounds, virtual screening, and drug toxicity. AI has proved its potential in the cheaper, easier, and timely development of drugs for different ailments. The application of AI not only enhances the quality of the drug development process but also assures better safety in the treatment and diagnosis of disease. AI introduces automation in drug development and clinical trials, minimizing the chances of human error. Focusing on the quality and quantity of the data, ethical consideration of the patient data may help in revolutionizing the process of synthetic drug development and treatment.