Conditioning Protein Generation via Hopfield Pattern Multiplicity
Small protein-family alignments often contain a subset of interest but not enough labeled data to train a conditional generator. We condition a training-free stochastic-attention sampler by adding one multiplicity ratio to its logits. Increasing this ratio shifts generation from the full family toward the designated subset. For unit-norm memories, the resulting Boltzmann distribution is exactly a Gaussian mixture whose component weights are set by the multiplicities. This result separates exact conditioning in latent space from losses caused by sampling, PCA reconstruction, and sequence decoding. Across five Pfam families, attention followed the analytic target, but recovery of single-res...
| Authors | Jeffrey D. Varner |
|---|---|
| Primary category | cs.LG |
| Published | 20 March 2026 |
| Last updated at source | 7 August 2026 |
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