MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization

MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/molmem-memory-augmented-agentic-reinforcement-learning-for-sample-efficient-molecular-optimization

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-15
Score updated: 2026-04-15
Score fresh until: 2026-05-15
References: 0
Source count: 4
Coverage: 67%

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization

PDF: https://arxiv.org/pdf/2604.12237v1

Repository: https://github.com/REAL-Lab-NU/MolMem

Source count: 4

Coverage: 67%

Last proof check: 2026-04-15T17:00:03.926Z

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Ready for execution: MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization

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Subject: MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization

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Claim map

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Evidencepartial
Sample-Efficient Molecular Optimization Ziqing Wang1 Yibo Wen1 Abhishek Pandey2 Han Liu1 Kaize Ding1* 1Northwestern University 2AbbVie {ziqingwang2029, yibowen2024}@u.northwestern.edu abhishek.pandey@abbvie.com {hanliu
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et al., 2024), ChemLLM (Zhang et al., 2024), PEIT-
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2025). Direct Retrieval and SFT-only also serve as ablations of MolMem. Notably, MolMem uses a compactQwen2.5-1.5Bbackbone, while most task- specific LLM baselines use 7–8B parameters
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molecules randomly sampled from ZINC-250k (Ir- win and Shoichet, 2005). We enforce two practical constraints: (1) a Tanimoto similarity threshold of γ= 0.4 to preserve similarity to the lead
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widens on bioactivity targets: 50.5% to 96.0% on
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require precise navigation of structure-activity rela- tionships, suggesting that MolMem is more effective on such complex landscapes. Smaller backbone, larger gains.Despite using a compact Qwen2.5-1.5B backbone
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Transformer) to maximize a scoring function un- der reward shaping. Apprentice baselines
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task-specific LLM baselines, we use a shared prompting template (Appendix D) and a common parsing and validation pipeline. Each model pro- duces candidate SMILES strings
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MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization

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