Explaining a molecular diffusion model

Diffusion models have gained considerable importance in many scientific fields. These models perturb the original data distributions by adding noise during the diffusion phase, which is then removed again during the subsequent denoising phase when generating the output. Predictions of diffusion are difficult to rationalize. Current efforts to explain the generative process and outputs of diffusion models are essentially limited to training data attribution for image generation. Herein, we report a feature attribution approach to better understand how diffusion models used in molecular design arrive at their results. As a proof of concept, we analyze an equivariant diffusion model for generating linkers in fragment-based compound design. The analysis yields plausible explanations for linker predictions and uncovers unexpected atomic contributions to generative design. Notably, the equivariant diffusion model does not require learning the underlying chemistry to produce chemically sound compound structures.