Artificial Intelligence Reshapes Drug Development: Technological Breakthroughs, Challenges, and Future Pathways

Artificial Intelligence (AI) is revolutionizing the drug development pipeline, significantly improving research and development (R&D) efficiency and success rates. AI's innovative applications span target identification, virtual screening, data integration, and molecular design. By utilizing advanced technologies such as deep learning, graph neural networks, and multimodal learning, AI facilitates the identification of disease targets, prediction of molecular binding modes, and integration of multi-omics data to construct dynamic models. Notable examples include AlphaFold-Multimer for protein structure prediction and Deep Docking for molecular docking. Despite these remarkable advancements, several formidable challenges persist and hinder the widespread adoption of AI in drug development. These include the "black-box" nature of AI models, inconsistent data quality, limited simulation of dynamic biological environments, and fragmented interdisciplinary knowledge. To overcome these obstacles, future developments should focus on three key areas: enhancing model interpretability through the strategic integration of physicochemical constraints, optimizing data sharing via the utilization of federated learning and differential privacy techniques, and constructing highly dynamic prediction frameworks by incorporating molecular dynamics simulations. With continued interdisciplinary collaboration and continuous technological innovations, AI holds the immense potential to reshape drug development, driving the progress of precision medicine, reducing R&D costs, and offering new approaches to addressing complex diseases.