Papers with molecular optimization
Feedback to Reasoning: LLM-Assisted Molecular Optimization with Domain Feedback and Historical Reasoning (2026.findings-acl)
Copied to clipboard
Wenhan Gao, Xiran Fan, Chin-Chia Michael Yeh, Jiarui Sun, Yuzhong Chen, Menghai Pan, Mahashweta Das, Yi Liu
| Challenge: | Existing methods for molecular optimization do not leverage domain feedback and historical knowledge with reasoning traces and chemical insights. |
| Approach: | They propose a conversational molecular optimization pipeline that enables LLMs to accumulate and retrieve past actions, rationales, and feedback. |
| Outcome: | The proposed framework transforms LLMs from passive text generators into agentic experts that learn both actions and reasoning from experience. |
MT-Mol: Multi Agent System with Tool-based Reasoning for Molecular Optimization (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Structured reasoning and tool-grounded molecular optimization are still underexplored. |
| Approach: | They propose a framework for molecular optimization that leverages tool-guided reasoning and role-specialized LLM agents. |
| Outcome: | a new framework outperforms existing LLM frameworks on 15 of 23 tasks. |
MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for molecular optimization use expensive oracle evaluations to achieve sample efficiency under limited oracular budget. |
| Approach: | They propose a framework that iteratively refines a lead compound to improve molecular properties while preserving structural similarity to the original molecule. |
| Outcome: | The proposed framework achieves 90% success on single-property tasks and 52% on multi-propety task using only 500 oracle calls. |