Molecular String Representation Preferences in Pretrained LLMs: A Comparative Study in Zero- & Few-Shot Molecular Property Prediction (2025.emnlp-main)
Copied to clipboard
| 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 . |
Similar Papers
A Survey of Large Language Models for Text-Guided Molecular Discovery: From Molecule Generation to Optimization (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language and symbolic notations. |
| Approach: | They analyze the current LLM learning paradigms to tackle four critical evaluation dimensions that have emerged as critical dimensions in recent studies. |
| Outcome: | The proposed models are able to interact with chemical spaces through natural language and symbolic notations, and have emerging extensions to incorporate multi-modal inputs. |
\mathtt{GeLLM^3O}: Generalizing Large Language Models for Multi-property Molecule Optimization (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have remarkable out-of-domain generalizability to novel optimization tasks. |
| Approach: | They propose a series of instruction-tuned LLMs for molecule optimization that outperform state-of-the-art instruction-based LLM models. |
| Outcome: | mathttMuMOInstruct outperforms state-of-the-art LLMs on 5 in-domain and 5 out-of domain tasks. |
Predicting Language Models’ Success at Zero-Shot Probabilistic Prediction (2025.findings-emnlp)
Copied to clipboard
Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Patiño, Ananya Joshi, Ruiqi Lyu, Jingjing Tang, Alistair Turcan, Khurram Yamin, Steven Wu, Bryan Wilder
| Challenge: | Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics. |
| Approach: | They conduct a large-scale empirical study of large language models’ zero-shot predictive capabilities across a wide range of tabular prediction tasks. |
| Outcome: | The results show that LLMs perform well on the base prediction task, and when they perform well, they are more likely to provide high-quality predictions. |
Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for molecule optimization fail to capture property-specific objectives . a series of instruction-tuned LLMs can perform targeted property-specific optimization . |
| Approach: | They propose a set of instruction-tuned LLMs that can perform targeted property-specific optimization. |
| Outcome: | a new instruction-tuned LLM can perform targeted property-specific optimization. |
Improving Chemical Understanding of LLMs via SMILES Parsing (2025.emnlp-main)
Copied to clipboard
| 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. |
Moleco: Molecular Contrastive Learning with Chemical Language Models for Molecular Property Prediction (2024.emnlp-industry)
Copied to clipboard
| 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. |
Dissecting Human and LLM Preferences (2024.acl-long)
Copied to clipboard
| Challenge: | a recent study shows that human and Large Language Model preferences are important for model fine-tuning and evaluation. |
| Approach: | They dissect the preferences of human and 32 different Large Language Models to understand their quantitative composition. |
| Outcome: | The proposed model is compared with 32 different large language models using real-world user-model conversations. |
How to Make Large Language Models Generate 100% Valid Molecules? (2025.emnlp-main)
Copied to clipboard
| 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. |
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | generative large language models are increasingly used for data augmentation tasks . text samples are mostly selected randomly and a comprehensive overview of other sample selection strategies is lacking. |
| Approach: | They compare random sample selection strategies and random sample sampling strategies to evaluate their effects in a low-resource setting. |
| Outcome: | The proposed model performance improvements are compared with other sample selection strategies. |
Characterizing Human and Zero-Shot GPT-3.5 Object-Similarity Judgments (2024.findings-naacl)
Copied to clipboard
| Challenge: | Recent advances in large language models have yielded few-shot, human-comparable performance on a range of tasks, but studies of LLM annotation accuracy and behavior are sparse. |
| Approach: | They characterize OpenAI’s GPT-3.5’s judgment on a behavioral task for implicit object categorization and give similarities and differences between them. |
| Outcome: | The proposed model augments human responses with LLMs for domains where data is sparse or compute resources are low. |