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.
Abstract
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
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
Molecule discovery is a pivotal research field, impacting everything from medicine to materials. Recently, Large Language Models (LLMs) have been widely adopted in molecular understanding and generation, serving as a bridge between the molecular space and the natural language space, yet the alignment between molecules and their corresponding captions remains a significant challenge. Previous endeavors typically treat molecules as monolithic inputs, lacking an intermediate reasoning process and sacrificing explainability. In this work, we define fine-grained alignments as the precise correspondence between a molecule’s sub-structures and the textual phrases that explain their properties. These alignments are crucial for LLMs to understand molecules in a more accurate and explainable manner. Normally, such fine-grained alignments require expert annotation, which is both costly and time-consuming. To allow LLMs to automatically label and learn the fine-grained alignments, we propose MolReFlect, a novel teacher-student framework, where a teacher LLM first generates and refines mappings between caption phrases and SMILES substructures and then explicitly teaches these detailed alignments to a student LLM. Experimental results demonstrate that MolReFlect enables LLMs to significantly outperform previous baselines, achieving the state-of-the-art performance in the molecule-caption translation task.
Jiatong Li, Yunqing Liu, Wei Liu et al.· IEEE Transactions on Knowled...· 13 citations
Knowledge Graph Construction (KGC) is essential for transforming unstructured text into structured knowledge representations. Despite advances in Large Language Models, existing methods treat KGC as a single-pass generation task, conflating extraction, normalization, and validation within a single forward pass. This leads to hallucinated facts, polysemous conflation, and fragmented triples, particularly in open-domain settings where predefined schemas are unavailable. In this work, we propose AgentsKG, a hierarchical multi-agent framework that decouples semantic perception from structural integration. In the Semantic Perception Layer, a multi-role Verification Committee filters hallucinated and invalid assertions through majority voting, while a Contextual Profiler resolves polysemous ambiguities by enriching mentions with context-dependent semantic descriptors. In the Structural Integration Layer, a Knowledge Linker merges redundant entities and relations based on semantic profiles, and an Ontological Logic Auditor enforces logical consistency across the graph. Extensive experiments demonstrate that AgentsKG outperforms state-of-the-art training-free baselines in both extraction accuracy and structural quality, offering a robust approach to open-domain knowledge graph construction without additional training. Source code is available at https://doi.org/10.5281/zenodo.20484211
Shilong Liu, Yongqiang Liu, Jiye Liu et al.· Proceedings of the 32nd ACM...· 0 citations
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.
Pengfei Liu, Shuang Ge, Xiaobo Wang et al.· Journal of Physical Chemistr...· 0 citations
This is the first method to expose GNN-derived attributions to an LLM as evidence for property prediction, and achieves the best overall results among generalist models and narrows the gap to specialist models tuned for each task.
Junwoo Park, Minyoung Shin, C. Lee et al.· 0 citations
FragBERTa is introduced, a molecular fragment-aware transformer-based representation language model pretrained using masked language modeling on Sequential Attachment-based Fragment Embedding (SAFE) representations, suggesting that fragment-based string representations offer advantages over atom-level representations for scaffold-sensitive and interaction-driven tasks.