Energy-Guided Diffusion for Valid SMILES Generation

Ivan Gurev, Nikolay Nikitin

Keywords: Diffusion Models, Molecular Generation, SMILES, Chemical Validity, Inference-Time Alignment

TL;DR: We fix chemical validity in SMILES diffusion models by steering their denoising process toward valid molecular structures during inference, without retraining.

Abstract:

Diffusion models provide a flexible framework for molecular generation, yet their
application to SMILES sequences is fundamentally constrained by chemical va-
lidity. In continuous diffusion over token embeddings, denoising trajectories of-
ten drift off the discrete manifold of valid SMILES, producing syntactic errors,
chemical violations, and corrupted stereochemistry after decoding. We analyze
these failure modes and show that they stem from a misalignment between smooth
probability paths in embedding space and the rule-governed structure of symbolic
molecular representations. We frame SMILES validity as an inference-time align-
ment problem and interpret valid generation as sampling from a tilted distribution
that reweights a base diffusion model toward structurally valid regions. Based on
this perspective, we introduce validity-aware diffusion mechanisms that combine
auxiliary training objectives with energy-based guidance during sampling, steer-
ing diffusion trajectories toward the valid SMILES manifold without changing
the underlying representation or retraining the base model. Experiments demon-
strate substantial improvements in SMILES validity while preserving diversity
and novelty, showing that inference-time aligned diffusion can be competitive with
autoregressive and masked language models for molecular string generation and
suggesting broader applicability to structured symbolic domains such as code and
discrete diffusion language models.

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Submission Number: 41