From Single-Step Edit Response to Multi-Step Molecular Optimization
Researchers from Wuhan University, Monash University, Zhejiang University, and Tongji University have proposed a novel approach for conditional molecular optimization aimed at achieving specified property shifts in molecules. Addressing the scarcity of structurally similar data and the instability of oracle-in-the-loop searches, the team introduced SMER-Opt. This method combines a single-step molecular edit response predictor (SMER) with a multi-step planner using guided tree search. By mining weakly related molecule pairs and decomposing structural differences into minimal edit units, the model converts endpoint property annotations into process-level supervision. This creates reusable action primitives and a directional edit evaluator that scores feasible edits based on their likelihood of driving desired property changes. The approach significantly reduces reliance on external evaluator queries during decision-making, offering a more efficient and stable solution for discrete edit optimization in drug discovery and chemical engineering contexts. The associated code has been made available via an anonymous repository, marking a significant advancement in applying artificial intelligence to complex molecular structure modifications.
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From Single-Step Edit Response to Multi-Step Molecular Optimization
Researchers from Wuhan University, Monash University, Zhejiang University, and Tongji University have proposed a novel approach for conditional molecular optimization aimed at achieving specified property shifts in molecules. Addressing the scarcity of structurally similar data and the instability of oracle-in-the-loop searches, the team introduced SMER-Opt. This method combines a single-step molecular edit response predictor (SMER) with a multi-step planner using guided tree search. By mining weakly related molecule pairs and decomposing structural differences into minimal edit units, the model converts endpoint property annotations into process-level supervision. This creates reusable action primitives and a directional edit evaluator that scores feasible edits based on their likelihood of driving desired property changes. The approach significantly reduces reliance on external evaluator queries during decision-making, offering a more efficient and stable solution for discrete edit optimization in drug discovery and chemical engineering contexts. The associated code has been made available via an anonymous repository, marking a significant advancement in applying artificial intelligence to complex molecular structure modifications.
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