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Explore recent advancements in GFlowNets for molecular discovery in this 56-minute conference talk by Emmanuel Bengio from Valence Labs. Delve into multi-objective optimization using goal-based strategies and improved training techniques for both de-novo discovery and lead optimization. Gain insights into GFlowNets' potential for scientific discovery and active learning. The talk covers training and parameterizing GFlowNets, multi-objective approaches, limitations of scalarization, goal-conditioned GFlowNets, evaluation metrics, learned focus models, and understanding GFlowNet training through minimal graph problems. Conclude with key takeaways and a Q&A session to deepen your understanding of this cutting-edge approach to molecular design.
