Papers with molecular property prediction
Moleco: Molecular Contrastive Learning with Chemical Language Models for Molecular Property Prediction (2024.emnlp-industry)
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| Challenge: | Pre-trained chemical language models (CLMs) are highly effective in predicting molecular properties, but implicitly contain limited structural information. |
| Approach: | They propose a contrastive learning framework to enhance the understanding of molecular structures within chemical language models by measuring the similarity of fingerprint vectors among different molecules. |
| Outcome: | The proposed framework outperforms state-of-the-art models in molecular property prediction. |
MolXPT: Wrapping Molecules with Text for Generative Pre-training (2023.acl-short)
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| Challenge: | Experimental results show that Generative pre-trained Transformers (GPT) have great success in natural language processing. |
| Approach: | They propose a unified language model of text and molecules pre-trained on SMILES wrapped by text. |
| Outcome: | The proposed model outperforms strong baselines of molecular property prediction on MoleculeNet and performs comparably to the best model in text-molecule translation while using less than half of its parameters. |
MCLE-Mol: Empowering LLM with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property Prediction (2026.findings-acl)
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Zhili Pu, Lantian Zhang, Hao Duan, Zhixing Zhang, Keyun Zhu, Yongqi Fan, Ruihui Hou, Tong Ruan, Yun Tang
| Challenge: | Large language models (LLMs) offer a new paradigm for molecular property prediction (MPP), yet a semantic gap between natural language and molecul representations limits their ability to capture structure–activity relationships (SAR). |
| Approach: | They propose an ML–LLM–Rule collaborative framework for MPP that injects ML-derived substructure attribution values into LLMs and calibrates them under specific chemical contexts. |
| Outcome: | The proposed framework outperforms baseline models on multiple benchmark datasets and is highly interpretable. |
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective on Molecule Graphs (2024.findings-emnlp)
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| Challenge: | Graph Neural Networks (GNNs) are successful in molecular property prediction tasks, but their outputs are often black-box and not easily understandable by humans. |
| Approach: | They propose a method to unleash the power of large language models (LLMs) to explain GNNs for molecular property prediction. |
| Outcome: | The proposed method uses autoencoder to generate the counterfactual graph topology from a set of counterfact text pairs based on an input graph. |
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models (2024.findings-acl)
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Xueliang Zhao, Xinting Huang, Tingchen Fu, Qintong Li, Shansan Gong, Lemao Liu, Wei Bi, Lingpeng Kong
| Challenge: | Multimodal reasoning is a key capability for large vision-language models . however, the vanilla Chain-of-Thought method fails to address critical steps in multi-step reasoning tasks. |
| Approach: | They propose a bi-modal Behavioral Alignment method to augment multimodal reasoning . they use domain-specific language to integrate multimodal information into a precise alternative form . |
| Outcome: | The proposed method significantly improves GPT-4V(ision) on geometry problem solving, chess positional advantage prediction and molecular property prediction. |
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. |