A Multitask Large Reasoning Model for Molecular Science

arXiv:2603.12808 · cs.LG · Submitted 2026-03-13 · Read on arXiv

cs.LG

Submitted: 2026-03-13

Updated: 2026-09-16

Code: https://github.com/AI-HPC-Research-Team/Mol-Reasoning

License: http://creativecommons.org/licenses/by/4.0/

The gist: Artificial intelligence in molecular science must move beyond pattern recognition toward chemically valid and interpretable reasoning.

Terminology

Abstract

Artificial intelligence in molecular science must move beyond pattern recognition toward chemically valid and interpretable reasoning. We present a task-adaptive large reasoning model that integrates chemical knowledge through a synergistic multispecialist architecture, chain-of-thought supervision, and molecule-informed reinforcement learning. Task-conditioned routing coordinates prediction and inference specialists across 10 molecular tasks spanning molecular description and generation, nomenclature translation, property prediction, and reaction prediction. The model outperforms more than 20 general-purpose and molecular large language models, improves aggregate performance over the base model by 50.3%, and surpasses the leading molecular multitask baseline on most tasks. Analyses of specialist representations and reasoning pathways reveal task-specific adaptation while retaining interpretable chemical inference. A case study further demonstrates an integrated workflow for central nervous system candidate generation, property screening, molecular interpretation, and retrosynthetic planning. These results demonstrate a versatile multi-task framework for knowledge-guided molecular reasoning and design, with the potential to serve as a core task engine for future molecular science agents.

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