This paper investigates compositionality in chemical language models (ChemLLMs). We introduce STEPS, a benchmark with compositional questions that reflect intricate chemical structures and reactions, to evaluate models’ understanding of chemical language. Our approach focuses on identifying and analyzing compositional patterns within chemical data, allowing us to evaluate how well existing LLMs can handle complex queries. Experiments with state-of-the-art ChemLLMs show significant performance drops in compositional tasks, highlighting the need for models that move beyond pattern recognition. By creating and sharing this benchmark, we aim to enhance the development of more capable chemical LLMs and provide a resource for future research on compositionality in chemical understanding.

The recent integration of chemistry with natural language processing (NLP) has advanced drug discovery. Molecule representation in language models (LMs) is crucial in enhancing chemical understanding. We propose Augmented Molecular Retrieval (AMORE), a flexible zero-shot framework for assessment of Chemistry LMs of different natures: trained solely on molecules for chemical tasks and on a combined corpus of natural language texts and string-based structures. The framework relies on molecule augmentations that preserve an underlying chemical, such as kekulization and cycle replacements. We evaluate encoder-only and generative LMs by calculating a metric based on the similarity score between distributed representations of molecules and their augmentations. Our experiments on ChEBI-20 and QM9 benchmarks show that these models exhibit significantly lower scores than graph-based molecular models trained without language modeling objectives. Additionally, our results on the molecule captioning task for cross-domain models, MolT5 and Text+Chem T5, demonstrate that the lower the representation-based evaluation metrics, the lower the classical text generation metrics like ROUGE and METEOR.