GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models

TL;DR

GlassMol introduces a concept bottleneck approach for molecular property prediction, enhancing interpretability without sacrificing accuracy by automating concept selection and leveraging large language models.

Contribution

The paper presents GlassMol, a novel, model-agnostic concept bottleneck model that addresses key challenges in applying interpretability to chemistry, improving transparency and trustworthiness.

Findings

GlassMol matches or exceeds black-box models in benchmarks.

Interpretability does not compromise predictive performance.

Automated concept curation enhances relevance and domain grounding.

Abstract

Machine learning accelerates molecular property prediction, yet state-of-the-art Large Language Models and Graph Neural Networks operate as black boxes. In drug discovery, where safety is critical, this opacity risks masking false correlations and excluding human expertise. Existing interpretability methods suffer from the effectiveness-trustworthiness trade-off: explanations may fail to reflect a model's true reasoning, degrade performance, or lack domain grounding. Concept Bottleneck Models (CBMs) offer a solution by projecting inputs to human-interpretable concepts before readout, ensuring that explanations are inherently faithful to the decision process. However, adapting CBMs to chemistry faces three challenges: the Relevance Gap (selecting task-relevant concepts from a large descriptor space), the Annotation Gap (obtaining concept supervision for molecular data), and the Capacity Gap (degrading performance due to bottleneck constraints). We introduce GlassMol, a model-agnostic CBM that addresses these gaps through automated concept curation and LLM-guided concept selection. Experiments across thirteen benchmarks demonstrate that \method generally matches or exceeds black-box baselines, suggesting that interpretability does not sacrifice performance and challenging the commonly assumed trade-off. Code is available at https://github.com/walleio/GlassMol.