Reimagining Drug Discovery with the Power of AI

Abstract

Artificial intelligence is transforming the pharmaceutical industry by discovering and developing drugs. These AI technologies speed up the processes with efficiency, precision, and innovation. This review discusses changes in AI at different stages of drug discovery and development. AI has enabled quick identification of potential candidates to become drugs through virtual screening, molecular modeling, and predictive analytics during the discovery phase. It involves the execution of a machine learning or deep learning algorithm over large datasets, analyzing those, finding patterns, and then predicting the properties in their drug candidates to significantly reduce time and cost. In the preclinical stages, AI predicts toxicity as well as efficacy through in vitro and in vivo data analysis by decreasing the chances of failure of the candidate during the later stages. AI also optimizes the clinical trials by having better recruitment to patients, monitoring, and data analysis. Algorithms would identify the right candidates who would guarantee diverse, representative samples of participants, monitor patient data in real time for adverse events and dosing adjustments, thereby translating into accurate and less variable results of the trials, speeding up the approval process.AI facilitates the regulatory approval process, whereby all documents of documents and huge amounts of paper work to be submitted can be scanned and organized into easily accessible files in no time. AI can monitor real-time data and detect adverse reactions to drugs. This will help in the post-marketing surveillance of medication. Some challenges are faced from AI, such as data quality, algorithmic bias, and regulatory hurdles. However, the promise it has is a good starting point for transforming drug discovery and development. Easy resolution of such problems would lead to full realizations of AI's benefits from pharmaceutical industry perspectives.