From Single-Step Edit Response to Multi-Step Molecular Optimization

2026-05-11

AI Summary

This work addresses the instability in oracle-in-the-loop search for conditional molecular optimization, which arises from a mismatch between supervisory signals and action-decision hierarchies. The authors propose a discrete optimization framework centered on edit-level response modeling. By decomposing property differences between molecular pairs into minimal editing units, they construct transferable action primitives and introduce a single-step molecular edit response predictor that, in conjunction with guided tree search, translates endpoint property discrepancies into process-level supervision. Chemical feasibility constraints and weakly associated molecular pair mining are integrated to substantially reduce reliance on external oracles. Experimental results demonstrate that the method achieves higher target property success rates and more chemically plausible editing pathways while requiring fewer oracle queries.

Abstract

Conditional molecular optimization aims to edit a molecule to realize a specified property shift. In practice, structurally similar molecule data is scarce, while decisions are inherently action-level: at each step, the system must select one local structural edit from a candidate set that is strictly filtered by chemical feasibility rules. This level mismatch between supervision and decision makes oracle-in-the-loop search unstable in molecular optimization. Regressing on property differences between molecule pairs improves data efficiency but relies on oracle-in-the-loop search, entangling transformation effects with global context and providing limited guidance for selecting the next feasible edit, often resorting to oracle-in-the-loop search. For this reason, we propose a response-oriented discrete edit optimization approach comprising two tightly coupled components: a single-step molecular edit response predictor (SMER) and a multi-step planner that composes local predictions into optimization trajectories via guided tree search (SMER-Opt). The approach learns a directional evaluation model over edit actions to support constraint-aware planning. It mines weakly related molecule pairs and decomposes their structural differences into minimal edit units, turning endpoint property annotations into process-level supervision and yielding reusable, transferable action primitives. A directional edit evaluator then scores feasible candidate edits by their likelihood of moving the molecule toward the desired property change, substantially reducing dependence on external evaluator queries at decision time. Code is available at https://anonymous.4open.science/r/SMER.

Problem

• conditional molecular optimization
• molecular editing
• oracle-in-the-loop
• data scarcity
• action-level decision

Innovation

• molecular optimization
• edit response prediction
• directional evaluation
• guided tree search
• action primitives