Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models

Understand

Providing explainable molecular property predictions is critical for many scientific domains, such as drug discovery and material science.

• Though transformer-based language models have shown great potential in accurate molecular property prediction, they neither provide chemically meaningful explanations nor faithfully reveal the molecular structure-property relationships.
• In this work, we develop a framework for explainable molecular property prediction based on language models, dubbed as Lamole, which can provide chemical concepts-aligned explanations.
• We take a string-based molecular representation -- Group SELFIES -- as input tokens to pretrain and fine-tune our Lamole, as it provides chemically meaningful semantics.