Research map / Probabilistic and scientific modeling / Molecules and proteins
Generative Models for Graph-Based Protein Design
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Structured Transformer vs SPIN2 | structure-conditioned sequence model · CATH Single chain | perplexity: 12.61 -> 9.03 | Helped |
| condition on structure vs unconditional LSTM | protein language model · CATH All | perplexity: 17.12 -> 6.85 | Helped |
| increase LSTM hidden size 128 -> 512 | unconditional LSTM · CATH All | perplexity: 17.13 -> 17.13 | No clear effect |
| add orientation edge features to distances (ablation) | Structured Transformer · CATH All | perplexity: 7.87 -> 6.85 | Helped |
| use backbone dihedrals instead of Cα angles as node features (ablation) | Structured Transformer · CATH All | perplexity: 7.83 -> 6.85 | Helped |
| replace attention with PairMLP aggregation (ablation) | Structured Transformer · CATH All | perplexity: 6.85 -> 6.55 | Helped |
| use rigid backbone features instead of flexible topological features (ablation) | Structured Transformer · CATH All | perplexity: 11.51 -> 6.85 | Helped |
| Structured Transformer vs Rosetta fixbb | fixed-backbone design · Single chain test set | native sequence recovery: 17.9 -> 27.6 | Helped |
| rigid vs flexible backbone features (ablation) | Structured Transformer · miniprotein mutation effects | Pearson R | Mixed |
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