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Proposes two complementary approaches to incorporate predictive uncertainty into reinforcement learning for chemical language models, improving robustness and increasing true hit rate by 0.25 in de novo molecular design.
Introduces Constrained Flow Optimization (CFO), a framework for fine-tuning generative flow models to maximize rewards while satisfying constraints in molecular design, with theoretical guarantees and experimental validation.
EPFL researchers developed Synthegy, an AI framework that uses large language models to guide chemical retrosynthesis and reaction mechanism analysis through natural language instructions, significantly improving strategic planning for chemists.
ChemAmp introduces a tool amplification paradigm that dynamically coordinates specialized chemistry tools (UniMol2, Chemformer) as composable agents to enhance performance on molecular tasks. The framework outperforms chemistry-specialized models and reduces inference token costs by 94% compared to vanilla multi-agent systems.