Papers with molecular representations
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning (2024.findings-acl)
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Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan
| Challenge: | BioT5+ is an extension of the BioT5, but lacked a nuanced understanding of molecular structures. |
| Approach: | They propose a new bio-entity modeling framework, BioT5+, which integrates IUPAC names and molecule data. |
| Outcome: | The proposed model bridges the gap between molecular representations and textual descriptions and improves the grounded reasoning of bio-text and bio-sequences. |
Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language (2025.findings-naacl)
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| Challenge: | Recent advances in AI for biological research focus on integrating molecular data with natural language to accelerate drug discovery. |
| Approach: | They propose a Language-based Automatic Annotation Augmentation framework that leverages large language models to augment existing datasets. |
| Outcome: | The proposed framework outperforms state-of-the-art models on text-based tasks and validates its versatility and utility. |
MT-Mol: Multi Agent System with Tool-based Reasoning for Molecular Optimization (2025.findings-emnlp)
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| Challenge: | Structured reasoning and tool-grounded molecular optimization are still underexplored. |
| Approach: | They propose a framework for molecular optimization that leverages tool-guided reasoning and role-specialized LLM agents. |
| Outcome: | a new framework outperforms existing LLM frameworks on 15 of 23 tasks. |
MolRAG: Unlocking the Power of Large Language Models for Molecular Property Prediction (2025.acl-long)
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| Challenge: | Recent LLMs exhibit limited effectiveness on molecular property prediction task due to semantic gap between representations and natural language and lack of domain-specific knowledge. |
| Approach: | They propose a framework that integrates Chain-of-Thought reasoning for molecular property prediction. |
| Outcome: | The proposed framework outperforms pre-trained LLMs on four datasets and matches supervised methods. |
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction (2024.emnlp-main)
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| Challenge: | Existing methods for chemical representation learning often lead to overfitting and limited scalability due to early convergence. |
| Approach: | They propose a framework to train Transformers on SMILES sequences to learn from structural examples and integrate external materials embedding to enrich molecular representations. |
| Outcome: | The proposed model outperforms state-of-the-art models on molecular property prediction tasks. |
MotifAgent: Learning Molecular Assembly through Multi-Agent Collaboration for Chemical Language Understanding (2026.findings-acl)
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| Challenge: | Existing approaches to molecular understanding are limited to static motif recognition without understanding connection rules governing how motifs assemble into valid topological structures. |
| Approach: | They propose a multi-agent reinforcement learning framework inspired by emergent collective intelligence to solve a problem where each motif is represented by an agent sharing a common LLM backbone. |
| Outcome: | Extensive experiments show that the proposed framework surpasses specialized expert models in molecular understanding tasks. |