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
Despite the potential of Large Language Models (LLMs) in chemical discovery, current LLMs still lack fundamental chemical domain knowledge, produce incoherent reasoning trajectories, and exhibit suboptimal performance across diverse chemical tasks. To address these challenges, we propose Chem-R, a general Chemical Reasoning model designed to emulate the deliberative processes of chemists. To build advanced reasoning capabilities of Chem-R, we design a three-phase training framework, including: 1) Chemical Foundation Training (CFT), which establishes core chemical knowledge. 2) Chemical Reasoning Protocol (CRP) Distillation, incorporating structured, expert-like reasoning traces to guide systematic and reliable problem solving. 3) Chemical Multi-Task Optimization (CMO) that optimizes the model for generalizable capabilities across diverse molecular- and reaction-level tasks. This structured pipeline enables Chem-R to achieve state-of-the-art performance on comprehensive benchmarks, surpassing leading LLMs, including Gemini-3-Pro and Kimi-k2.5, by up to 19% on molecular tasks and 40% on reaction tasks. Meanwhile, Chem-R also consistently outperforms existing chemical foundation models across both molecular and reaction level tasks. These results demonstrate Chem-R's superior generalization, interpretability, and potential as a foundation for next-generation AI-driven chemical discovery. The code and model are available at https://github.com/davidweidawang/Chem-R.
Weida Wang, Benteng Chen, Di Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
Abstract Motivation Molecular captions, patents, and medicinal-chemistry notes describe substructures and their relations in natural language, whereas computational models operate on formal representations such as SMILES. Bridging this semantic–structural gap is important for patent interpretation, structural relationship analysis, and controllable molecular editing, yet current large language models struggle to ground textual references to precise molecular components. Results We propose RelAgent, a cooperative multi-agent framework for molecular relationship grounding. 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. This design supports fine-grained reasoning over molecular substructure and substantially improves performance on the MolGround benchmark. RelAgent achieves 81.4% entity-extraction F1, 56.0% exact-match localization F1, and 54.6% relationship F1 on an open-source LLaMA3.1-8B model, improving the REL F1 from 0.1% to 54.6% and exceeding the vanilla Gemini-3.1-Pro baseline in our experiments. These results indicate that agentic, structure-aware reasoning is a practical direction for interpretable molecular understanding in bioinformatics. Availability and implementation The source code for RelAgent is available at https://github.com/Anya-RB-Chen/RelAgent
Rubing Chen, Jiaxin Wu, C. Zhang et al.· Bioinformatics· 0 citations
Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations. Learning the mechanism logic is therefore essential for enhancing the fundamental chemical intelligence of large language models (LLMs). The stepwise deduction of reaction mechanism aligns naturally with the reasoning paradigms of reasoning LLMs. However, current chemical LLMs primarily emphasize coarse-grained name reactions for product prediction and retrosynthesis, often leading to physical inconsistencies and hallucinations. In contrast, specialized small-scale generative models for mechanism inference typically suffer from restricted generalization capacity across diverse chemical spaces. To overcome these limitations, we built a novel, large-scale reasoning dataset of reaction mechanisms. Furthermore, we established the FukuyamaBench, a difficult benchmark derived from Fukuyama's Advanced Organic Reaction Mechanism book, to rigorously evaluate model performance on hierarchical mechanism reasoning. Our fine-tuned Qwen3-30B-A3B achieves 8.3% exact pathway match on FukuyamaBench Set~A, surpassing the specialized FlowER model (5.1%), demonstrating that mechanism-aware training substantially enhances chemical reasoning in language models.
Xingyu Dang, Haocheng Tang, Junmei Wang et al.· arXiv.org· 0 citations
Artificial intelligence in science is undergoing a foundational change. Rather than serving as a passive analytical instrument — classifying images, predicting structures, spotting patterns — AI systems are beginning to act as autonomous research collaborators. These systems, built on large language models and tool-integrated architectures, can reason about experimental design, formulate strategies, execute multi-step workflows, and refine their approaches from empirical feedback. Often called “AI Scientists,” they participate across the full research lifecycle, from the seed of a hypothesis through to a draft manuscript. This article examines the emerging paradigm of agentic AI for scientific discovery. It 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. A central concern of the analysis is the verification crisis — the growing gap between what these systems can produce and what they can prove. We compile quantitative evidence on failure rates, analyse competing frameworks for trustworthy agentic science (Chain-of-Evidence, Audit-Closed protocols, FEV, and structural FDR enforcement), and propose actionable standards for rigorous validation. The article closes with an assessment of the field’s limitations and a set of priority directions for making agentic science trustworthy at scale.
Alexander Taktakidze· Longevity Horizon· 0 citations
Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through scientific principles and physical constraints, from stereochemistry and bonding to symmetry, energetics and periodic order. However, applying artificial intelligence to this process presents a joint challenge of representation and reasoning: models must preserve domain-native structural information while showing how specific evidence supports predictions under these constraints. Here we introduce SciReasoner, a multimodal scientific foundation model for native structural reasoning across proteins, small molecules and inorganic crystals. SciReasoner discretizes coordinates, topologies and periodic connectivities into a unified structure-aware vocabulary, treating structural tokens as addressable evidence units during reasoning. In homology-controlled Gene Ontology prediction, SciReasoner improves Cellular Component annotation for low-homology and orphan-like proteins, increasing $F_{\max}$ from 0.42 to 0.55. In chemistry, it raises single-step retrosynthesis accuracy from 0.63 to 0.72 while generating fragment-level disconnection and precursor-verification traces. In materials science, its representations separate elemental and compound phases and resolve high- and low-band-gap regimes. Across 86 benchmarks, SciReasoner achieves state-of-the-art performance on 67 tasks. Double-blind expert evaluation rates its reasoning traces as preferred or at least comparable to those of a frontier large language model in 98% of cases. By making structure an inspectable substrate for reasoning under scientific constraints, SciReasoner connects accurate prediction with interpretable scientific inference.
Chen Tang, Yizhou Wang, Jianyu Wu et al.· 1 citation