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| Metric | Value |
|---|---|
| Spearman $ | |
| ho$ | 0.89 |
| Pearson $r$ | 0.88 |
| MAE | 0.53 kcal/mol |
1import torch
2from ddg_vae import DDGVAE
3from ddg_mlp_refiner import DDGMLPRefiner
4
5# 1. Load VAE base
6vae = DDGVAE.create_protherm_variant(use_hyperbolic=False)
7vae_ckpt = torch.load("vae_protherm.pt", map_location="cpu")
8vae.load_state_dict(vae_ckpt["model_state_dict"])
9
10# 2. Load MLP Refiner
11refiner = DDGMLPRefiner(latent_dim=32, hidden_dims=[64, 64, 32])
12ref_ckpt = torch.load("pytorch_model.bin", map_location="cpu")
13refiner.load_state_dict(ref_ckpt["model_state_dict"])
14
15# 3. Predict
16vae.eval(); refiner.eval()
17with torch.no_grad():
18 vae_out = vae(mutation_features)
19 mu = vae_out["mu"]
20 vae_pred = vae_out["ddg_pred"]
21 refined = refiner(mu, vae_pred)
22 ddg = refined["ddg_pred"]
23
24print(f"Predicted DeltaDeltaG: {ddg.item():.4f} kcal/mol")1@software{ddg_refiner_2026,
2 author = {AI Whisperers},
3 title = {DDG ProTherm Refiner: VAE-Guided Stability Predictor},
4 year = {2026},
5 url = {https://huggingface.co/ai-whisperers/ddg-protherm-refiner-sota}
6}