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MolReAct is introduced, which reformulates molecular optimization as search over compact reaction spaces proposed by a tool-augmented large language model (LLM) to improve long-term decision-making over multiple reaction steps.
Reaction virtual screening and discovery are fundamental challenges in chemistry and material science, where traditional graph neural networks (GNNs) struggle to model multi-reactant interactions. In this work, we propose ChemHGNN, a hypergraph neural network (HGNN) framework that effectively captures high-order relati...
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