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ARXIV:2605.10035 · MOLECULAR OPTIMIZATION · SUBMITTED 12 MAY · 20:16 UTC · FRESHNESS FRESH
ARXIV:2605.10035MOLECULAR OPTIMIZATIONSUBMITTED 12 MAY · 20:16 UTCFRESHNESS FRESHHaojie Rao · Kun Li · Yida Xiong · Jiameng Chen · Wenbin Hu · Yizhen Zheng · +2 at arXiv
A discrete edit optimization approach for molecular property shifts using a single-step predictor and multi-step planner.
Opportunity summary
Pain A discrete edit optimization approach for molecular property shifts using a single-step predictor and multi-step planner.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A discrete edit optimization approach for molecular property shifts using a single-step predictor and multi-step planner. In practice, structurally similar molecule data is scarce, while decisions are inherently action-level: at each step, the system…
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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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…
Molecular Optimization moved forward this cycle; last verified May 2026. Public score 4.0/10.
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A discrete edit optimization approach for molecular property shifts using a single-step predictor and multi-step planner.
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10.48550/arXiv.2605.10035A discrete edit optimization approach for molecular property shifts using a single-step predictor and multi-step planner.
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.
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Dimensions overall score 4.0
PROBLEM
A discrete edit optimization approach for molecular property shifts using a single-step predictor and multi-step planner. In practice, structurally similar molecule data is scarce, while decisions are inherently action-level: at each step, the system must select one local struct...
METHOD
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 candida...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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 guid...
WHY NOW
Molecular Optimization moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A discrete edit optimization approach for molecular property shifts using a single-step predictor and multi-step planner. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Molecular Optimization moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A discrete edit optimization approach for molecular property shifts using a single-step predictor and multi-step planner.
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Molecular Optimization
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Commercial read
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