Artificial Intelligence in Drug Discovery and Clinical Trails

Abstract

Artificial Intelligence (AI) is an important technology in drug discovery and clinical trials, particularly for handling and analysis of large and complex chemical, biological, and clinical datasets. Machine learning, deep learning, natural language processing, reinforcement learning, and generative AI are being applied across the various stages of drug development. In drug discovery, AI supports target identification and validation, virtual screening, molecular docking, de novo drug design, ADMET prediction, drug repurposing, and prediction of drug–target interactions. In clinical trials, AI can assist with patient recruitment and stratification, biomarker identification, trial design and simulation, site selection, real-time safety monitoring, and clinical data analysis. Emerging approaches such as digital twins, synthetic control arms, multimodal AI, large language models, and AI-driven automated laboratories may further improve the efficiency of drug development. However, challenges including data quality and availability, algorithmic bias, limited interpretability, data privacy and security, interoperability, regulatory uncertainty, and the need for experimental and clinical validation remain important barriers. Therefore, AI is considered as a supportive tool that works with researchers and healthcare professionals. Continued development of reliable, transparent, and ethically governed AI systems may contribute to faster, more efficient, and data-driven drug discovery and clinical trial processes.