AI in Drug Discovery: Applications, Challenges and Future Prospects

Abstract

Traditional pharmaceutical R&D is constrained by substantial financial investment, lengthy development cycles, and a high probability of failure. Artificial intelligence (AI) is now being incorporated into multiple stages of the pharmaceutical pipeline, including target identification, molecular design, synthesis planning, and clinical research. This paper reviews how machine learning, deep learning, natural language processing, and related computational methods are being applied across the drug discovery process. Particular attention is given to AlphaFold-based protein structure prediction, AI-supported virtual screening, generative chemistry, retrosynthetic planning, digital pathology, and the use of real-world clinical data. The review also considers limitations that are often hidden by strong computational performance, such as incomplete training data, limited interpretability, weak interoperability, uncertain external validity, and the continuing need for laboratory and clinical confirmation. In addition, several practical examples from industry and academic research are discussed to connect technical principles with their actual use in pharmaceutical development. Future progress is likely to depend on multimodal data integration, explainable models, robotic design-make-test-analyze cycles, privacy-preserving collaboration, and regulatory frameworks that evaluate both model performance and the quality of the evidence generated. AI should therefore be understood as an augmentation technology: it can prioritize hypotheses and accelerate iteration, but it cannot replace biological reasoning, experimental judgment, or clinical responsibility.