arXiv Machine Learning October 28, 2026 By Sarabeshwar Balaji, Ritesh Kumar, Lav R. Varshney

Interpretable and Steerable Molecular Graph Generation using Concept Bottlenecks

Read the original on arXiv Machine Learning →

The paper introduces concept bottlenecks into deep graph generators to enhance interpretability and steerability in molecular graph generation. By embedding human-interpretable concepts, the authors demonstrate improved distributional fidelity (lower FCD) while maintaining validity and uniqueness. They also present a Concept Controller optimization method for steering generation, evaluated within a one-shot molecular graph generation framework.

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