Artificial Intelligence in Drug Development: A Comprehensive Review

Abstract

The drug discovery process has always been linked to prolonged development time, high expenses, and an increased risk of failure. One of the key constraints of existing strategies is their dependency on trial and error and a poor predictability. AI technologies have proven to be very promising solutions that can significantly enhance pharmaceutical studies by means of evidence-based decision making and predictive modeling. ML, DL, and other approaches are widely used for the analysis of biological and chemical databases within the drug discovery process. Target identification, molecular design, optimization, pharmacokinetics predictions, clinical testing, and post-market surveillance represent some areas where AI is successfully applied. AI allows for swift processing of vast amounts of biological and chemical information that leads to the discovery of novel drugs and improvement of drug properties like solubility and bioavailability. There are still certain drawbacks that need to be addressed, like the issue of data bias or transparency problems. This paper provides a critical overview of the use cases, benefits, disadvantages, and perspectives of AI Application in Drug Development.