Mathematical and Artificial Intelligence Techniques in Modern Drug Discovery: A Review

The future of drug research is intrinsically connected to the continuous advancement and prudent integration of Artificial intelligence (AI) and mathematics. By addressing challenges and leveraging possibilities, the pharmaceutical industry may fully harness the potential of AI to develop innovative and effective treatments for diverse diseases. In our opinion, finding new drugs takes long time and funds, and in the past, it was mostly done manually. AI has changed this sector completely, making drug manufacturing faster, cheaper, and more specific. This review presents the pertinent literature on drug discovery utilizing mathematical modeling and AI tools and methodologies implemented at every stage of drug development to expedite the research process and mitigate risk and costs in clinical trials, it also presents that how mathematical modeling and AI algorithms can be used together at several stages of drug development. There is a lot of focus on how mathematical frameworks like Linear Algebra, optimization, statistical modeling, graph theory and differential equations may operate along with the techniques of AI like machine learning (ML), deep learning (DL), reinforcement learning (RL), natural language processing (NLP) and transfer learning (TL) and the problems that are now being faced, the tools and datasets that are accessible, and what the future holds for this field, which is changing quickly.