My presentation will explore how artificial intelligence is revolutionizing drug discovery by accelerating hit identification, optimizing molecular design, and enhancing predictive modeling. I will highlight advancements in AI-driven docking, molecular property prediction, and active learning strategies that enable more efficient screening of ultra-large chemical libraries. By integrating deep learning with structure-based and ligand-based approaches, AI is transforming traditional workflows, reducing costs, and increasing success rates in early-stage drug development. The session will include insights into cutting-edge molecular generative models, such as Flow Networks out of Yoshua Bengio's lab, and their applications in real-world pharmaceutical research.
