EvoIF Protein Predictor Fuses Dual Evolutionary Signals Using 0.15% Data
BiologyAIDaily · x · 2026-07-22
The post details EvoIF, a lightweight protein fitness predictor that improves accuracy by explicitly fusing within-family evolutionary profiles with cross-family structural evolutionary signals.
Key Highlights:
- Lightweight & Efficient: Keeps large backbones (ESM-2 & ProteinMPNN) frozen, training only a compact graph encoder. It uses merely 0.15% of the training data required by large baselines while offering faster training.
- Theoretical Innovation: Frames natural evolution as implicit reward maximization, linking masked language modeling (MLM) to maximum-entropy inverse reinforcement learning (IRL) to explain its efficacy in zero-shot prediction.
- Performance: Achieves competitive results on the ProteinGym benchmark (>2.5M mutants), scoring Spearman 0.489 (MSA-free) and 0.519 (MSA-enabled).
Related event: EvoIF Model Enhances Protein Fitness Prediction(2 posts)→
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