Faculty of Pharmacy, Dr. Babasaheb Ambedkar Technological University, Raigad, Lonere
Artificial Intelligence (AI) is transforming the pharmaceutical industry by accelerating drug discovery, improving prediction accuracy, and reducing development costs. Traditional methods of drug design are time-consuming, costly, and often limited by human interpretation. AI integrates computational modeling, big data analytics, and machine learning (ML) to revolutionize each phase of drug discovery from target identification to clinical testing. This review explores the mechanisms, technologies, and tools behind AI in drug discovery and evaluates its potential to enhance pharmaceutical research. Applications include target validation, de novo drug design, virtual screening, and toxicity prediction. Case studies such as Alpha Fold and Scientia demonstrate how AI reduces RCD timelines and enhances molecular precision. Challenges like data bias, interpretability, and regulatory gaps are also discussed. The review concludes that integrating AI with experimental pharmacology and omics data will establish a new paradigm of precision drug discovery.
Drug discovery traditionally requires over a decade and billions of dollars to bring a single molecule to market. The process involves identifying potential therapeutic targets, screening compound libraries, optimizing hits, and conducting pre-clinical and clinical trials. However, the success rate remains extremely low, with only about 1 in 10,000 Compound reaching approval. AI has emerged as a powerful tool to address these inefficiencies. By mimicking human cognitive processes through algorithms, AI enables machines to analyze chemical structures, biological data, and clinical outcomes to derive meaningful insights faster than traditional computational methods. In pharmacy, this integration enhances prediction of pharmacokinetics, toxicity, and molecular interactions helping researchers focus on the most promising candidates. AI-driven discovery utilizes machine learning (ML), deep learning (DL), and natural language processing (NLP) to extract patterns from diverse datasets. Pharmaceutical companies and academic institutions increasingly adopt AI for drug repurposing, target discovery, and predictive modeling. According to a 2023 report, over 150 AI-based collaborations between pharma and tech companies have been established worldwide. [1] Drug discovery has always been a long, expensive, and uncertain scientific journey. Traditional research methods involve repeated laboratory experiments, large clinical trials, and years of trial-and-error before a single promising drug reaches the market. Because biological systems are extremely complex, researchers often struggle to predict how a new molecule will behave in the human body. As a result, despite decades of scientific progress, the majority of drug candidates still fail during development mainly due to poor efficacy, toxicity issues, or unexpected side effects (Ekins et al., 2019). In recent years, Artificial Intelligence (AI) has emerged as a revolutionary solution to these challenges. AI brings the ability to simulate biological processes, analyze millions of chemical compounds, and predict drug behavior much faster than conventional computational tools. Instead of relying solely on laboratory experiments, scientists can now use AI models to screen compounds virtually, identify molecular interactions, and make early decisions that save both time and resources. This shift represents a major transformation in how pharmaceutical research is conducted.
Dr. Sunil Jaybhaye*, Payal Kundkar, Nikita Pungle, Artificial Intelligence in Drug Discovery: Challenges and Future Direction in Pharmaceutical Research, Int. J. Sci. R. Tech., 2025, 2 (12), 224-232. https://doi.org/10.5281/zenodo.17929314
10.5281/zenodo.17929314
