[2603.20262] Deciphering Scientific Reasoning Steps from Outcome Data for Molecule Optimization

Quantitative Biology > Biomolecules arXiv:2603.20262 (q-bio) [Submitted on 13 Mar 2026] Title:Deciphering Scientific Reasoning Steps from Outcome Data for Molecule Optimization Authors:Zequn Liu, Kehan Wu, Shufang Xie, Zekun Guo, Wei Zhang, Tao Qin, Renhe Liu, Yingce Xia View a PDF of the paper titled Deciphering Scientific Reasoning Steps from Outcome Data for Molecule Optimization, by Zequn Liu and 7 other authors View PDF HTML (experimental) Abstract:Emerging reasoning models hold promise for automating scientific discovery. However, their training is hindered by a critical supervision gap: experimental outcomes are abundant, whereas intermediate reasoning steps are rarely documented at scale. To bridge this gap, we propose DESRO, a framework for deciphering scientific reasoning from outcomes. By analyzing shared patterns and key differences within grouped data, a large language model (LLM) can recover the underlying logic. We instantiate this framework in molecule optimization, a pivotal stage in drug discovery that traditionally relies on the iterative reasoning of medicinal chemists. Across 2.3 million molecular property records, our framework infers optimization rationales by grouping molecules with shared fragments, then using an LLM to analyze how structural variations correlate with property differences. Based on the derived data, we train a model that conducts molecule optimization through an interpretable reasoning process. DESRO achieves the highest success ra...