AI in Drug Development: Applications and Challenges

Abstract

The drug discovery process is a long and complicated one, involving multiple steps and issues, starting from target identification and ending with clinical development. The amount of chemical, biological, and clinical data produced by modern pharmaceutical companies is tremendous, and there is a continuous need to develop new approaches that could allow for efficient data mining and identification of relevant information. In this regard, artificial intelligence (AI), and specifically machine learning (ML) and deep learning (DL) seem to be an attractive choice for researchers for tackling the challenging task of data analysis. In silico artificial intelligence has been applied at all stages of the drug discovery and development pipeline, ranging from molecular target identification, virtual screening, molecular docking, quantitative structure-activity relationship, ADMET prediction, de novo design, ligand and lead optimisation, computer-assisted synthesis design, drug repurposing, and clinical trials. These approaches can facilitate the exploration of large data sets and the prioritisation of compounds for experimental assessment, thereby reducing the time and cost associated with traditional drug discovery. and expensive traditional drug discovery. However, despite the reported benefits and opportunities, there are still some limitations associated with AI-driven drug discovery, including dependence on large data sets with diverse characteristics, insufficient data for model training, data bias, overfitting, lack of interpretability, absence of independent evaluation, and regulatory issues. Therefore, it is essential to view AI technologies as an instrumental but supplementary part of decision-making in pharmaceutical research and development rather than a fully independent alternative to traditional hit- and lead-discovery strategies The manuscript is a Review article that focuses on the main areas of application of artificial intelligence for drug discovery and development, including benefits, drawbacks, ethical issues, and opportunities.