arXiv:2607.02140v1 Announce Type: new Abstract: Chemical language models (CLMs) are trained with linearized representations such as SMILES, yet it remains unclear which chemically meaningful substructures they encode. To foster a better understanding of CLMs, we conduct a systematic study and probe for 78 molecular substructures across eight pre-trained and six randomly initialized models. We furthermore study how fine-tuning on chemical downstream tasks affects the learned representations of molecular substructures. Our results show that pre-training generally improves molecular structure awa
The proliferation of AI models in scientific domains necessitated a deeper understanding of their internal representations, especially in complex areas like chemistry, driving this research into CLM explainability.
Understanding how chemical language models encode molecular structures is critical for accelerating drug discovery, materials science, and synthetic biology, moving beyond black-box applications to more guided design.
This research provides methods and insights to evaluate and potentially improve the chemical intuition of AI models, shifting from mere performance metrics to an analysis of learned chemical meaning.
- · Pharmaceutical companies
- · Materials science
- · Chemical engineering
- · AI explainability researchers
- · Black-box AI model developers (without explainability features)
Improved interpretability of chemical language models will lead to more effective and trustworthy AI tools in chemistry.
Accelerated discovery and design of novel drugs, materials, and catalysts become possible through better-understood AI representations.
The integration of explainable AI into scientific workflows could fundamentally change research paradigms, empowering AI to serve as a more intuitive partner, rather than just an opaque prediction engine.
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Read at arXiv cs.LG