Papers with SMILES
Transformer-based Approach for Predicting Chemical Compound Structures (2020.aacl-main)
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| Challenge: | Existing methods to predict chemical compound structures from their names are limited and use handcrafted rules. |
| Approach: | They propose a Transformer-based model that predicts SMILES strings from chemical compound names instead of handcrafted rules. |
| Outcome: | The proposed model achieves higher F-measures than the existing model and the existing one. |
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. |
Molecular String Representation Preferences in Pretrained LLMs: A Comparative Study in Zero- & Few-Shot Molecular Property Prediction (2025.emnlp-main)
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| Challenge: | Molecular property prediction plays a crucial role in medicinal chemistry . traditional machine learning approaches do not involve natural language . |
| Approach: | They compare performance of four state-of-the-art LLMs on molecular property prediction tasks . they find statistically significant zero- and few-shot preferences for InChI and IUPAC names . |
| Outcome: | The proposed model outperforms the current model on molecular property prediction tasks . the model's representation preferences are based on representation granularity, tokenization and prevalence in pretraining corpora . |
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. |
Multilingual Molecular Representation Learning via Contrastive Pre-training (2022.acl-long)
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| Challenge: | Molecular representation learning is an essential role in cheminformatics, but language model-based approaches focus on local features, hence they may not capture global information. |
| Approach: | They propose a multilingual molecular embedding generation approach that uses two different languages to train a given molecule. |
| Outcome: | The proposed approach is pre-trained using SMILES and IUPAC as two different languages on large-scale molecules. |
Improving Chemical Understanding of LLMs via SMILES Parsing (2025.emnlp-main)
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| Challenge: | Molecular string representations such as SMILES and SELFIES are becoming a standard format for applying large language models (LLMs) however, molecular strings follow complex syntactic rules for encoding molecules, which LLMs struggle to interpret. |
| Approach: | They propose a framework that parses SMILES into clean and deterministic tasks to promote graph-level molecular comprehension. |
| Outcome: | The proposed framework improves structural comprehension and competes with the baseline on the Mol-Instructions benchmark. |
Rethinking NLP for Chemistry: A Critical Look at the USPTO Benchmark (2025.findings-emnlp)
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| Challenge: | Natural Language Processing (NLP) has revolutionized computer-aided synthesis planning by reframing chemical synthesis prediction as a sequence-to-sequence modeling problem over molecular string representations like SMILES. |
| Approach: | They propose to reframe chemical synthesis prediction as a sequence-to-sequence modeling problem over molecular string representations like SMILES. |
| Outcome: | The proposed framework yields impressive benchmark scores on the USPTO dataset, a large corpus of reactions extracted from US patents. |
How to Make Large Language Models Generate 100% Valid Molecules? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) can learn to perform a wide range of tasks, but generating valid molecules using representations like SMILES is challenging in few-shot settings. |
| Approach: | They propose a language framework that converts invalid SMILES to SELFIES and LLMs as post-hoc correctors to ensure that the molecules generated by LLM are 100% valid. |
| Outcome: | The proposed model performs worse with SELFIES than with SMILES and improves on other metrics. |