Aug 2026· Journal of Physical Chemistry Letters· 0 citations· 21 references
TL;DR
A task-adaptive large reasoning model that integrates chemical knowledge through a synergistic multispecialist architecture, chain-of-thought supervision, and molecule-informed reinforcement learning is presented, demonstrating a versatile multitask framework for knowledge-guided molecular reasoning and design.
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 multitask framework for knowledge-guided molecular reasoning and design, with the potential to serve as a core task engine for future molecular science agents.
Automation is transforming scientific discovery by enabling systematic exploration of complex hypotheses. Large language models (LLMs) perform well across diverse tasks and promise to accelerate research, but often struggle with logical structures. Here, we present a framework for biological discovery integrating LLM-based agents with laboratory automation, guided by logical scaffolds incorporating symbolic relational learning, structured vocabularies and experimental constraints. This integration improves coherence and reliability in automated workflows. We couple this AI-driven approach to automated cell-culture and metabolomics platforms, enabling integrated hypothesis validation and refinement, yielding a flexible discovery system. The system identified novel interactions in Saccharomyces cerevisiae, including glutamate-induced growth inhibition in spermine-treated cells and aminoadipate's partial rescue of formic-acid stress. All hypotheses, experiments and data are captured in a graph database employing controlled vocabularies. Existing ontologies are extended, and a novel representation of scientific hypotheses is presented using description logics. This work demonstrates the potential for a reliable machine-driven discovery process in systems biology.
Daniel Brunnsåker, Alexander H. Gower, Prajakta Naval et al.· Journal of the Royal Society...· 2 citations
The proposed Chem-R, a general Chemical Reasoning model designed to emulate the deliberative processes of chemists, achieves state-of-the-art performance on comprehensive benchmarks, surpassing leading LLMs, including Gemini-3-Pro and Kimi-k2.5.
Weida Wang, Benteng Chen, Di Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
RelAgent decomposes the task into three interpretable stages: entity extraction, substructure localization, and ontology-guided relationship reasoning, and then uses verifier agents to rank structurally plausible candidates to support fine-grained reasoning over molecular substructure.
Rubing Chen, Jiaxin Wu, C. Zhang et al.· Bioinformatics· 0 citations
A novel, large-scale reasoning dataset of reaction mechanisms, and the FukuyamaBench, a difficult benchmark derived from Fukuyama's Advanced Organic Reaction Mechanism book, to rigorously evaluate model performance on hierarchical mechanism reasoning, demonstrate that mechanism-aware training substantially enhances chemical reasoning in language models.
Xingyu Dang, Haocheng Tang, Junmei Wang et al.· arXiv.org· 0 citations
This article examines the emerging paradigm of agentic AI for scientific discovery, traces the conceptual shift from tools to agents, lays out a six-stage workflow spanning literature synthesis to manuscript generation, and reviews practical systems in chemistry, equation discovery, materials science, and general machine learning research.
Alexander Taktakidze· Longevity Horizon· 0 citations
SciReasoner is introduced, a multimodal scientific foundation model for native structural reasoning across proteins, small molecules and inorganic crystals that connects accurate prediction with interpretable scientific inference.
Chen Tang, Yizhou Wang, Jianyu Wu et al.· 1 citation