Artificial intelligence (AI) for drug discovery and optimization

Artificial intelligence (AI) is rapidly transforming drug discovery and development, shifting from traditional trial-and-error approaches to datadriven, predictive, and highly efficient workflows. Early AI applications in the 1980s–1990s focused on simple molecular modeling, evolving through the 2000s with machine learning for improved molecular interaction predictions. The 2010s saw accelerated growth due to advances in big data, deep learning, and large-scale biological datasets, enabling AI integration across target identification, drug repurposing, lead optimization, and virtual screening. AI techniques—including natural language processing, reinforcement learning, swarm intelligence, and generative adversarial networks—enhance molecular design and optimization, overcoming the vast complexity of chemical space. Applications extend to biomarker discovery, clinical trial design, and personalized medicine. While AI improves predictive accuracy, reduces costs, and accelerates timelines, challenges remain, including data quality issues, biological complexity, interpretability, regulatory constraints,and the irreplaceable role of human expertise. Opportunities lie in combining AI with real-world data, improving decision-making, and fostering innovative therapeutic solutions. A balanced approach—leveraging AI’s strengths while addressing its limitations—is crucial for its responsible and sustainable integration into pharmaceutical research.