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Model-Mega-sacle-Protein-Stability-Prediction-35M
SaProtHub/Model-Mega-sacle-Protein-Stability-Prediction-35M
Model-Mega-sacle-Protein-Stability-Prediction-35M is a machine learning model from SaProtHub. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
This model is used to predict protein stability (ΔΔG) for mutant amino acid sequence.
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At a glance
- Library
- peft
- Access
- Public
- Created
- Jul 17, 2024
- Updated
- Oct 16, 2024
- SHA
- 5ca18adb
Base models
Model card
From the Hugging Face model README
Base model: westlake-repl/SaProt_35M_AF2
Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->This model is used to predict protein stability (ΔΔG) for mutant amino acid sequence.
Task type
protein level regression
Dataset description
The dataset is from Mega-scale experimental analysis of protein folding stability in biology and design. We collect all protein sequences that have ΔΔG value.
Label is the ΔΔG (kcal/mol) value, the positive value means stable and the negetive value represents unstable, ranging from minus infinity to positive infinity.
Model input type
Amino acid sequence
Performance
test_loss: 0.18
test_spearman: 0.92
LoRA config
lora_dropout: 0.0
lora_alpha: 16
target_modules: ["query", "key", "value", "intermediate.dense", "output.dense"]
modules_to_save: ["classifier"]
Training config
class: AdamW
betas: (0.9, 0.98)
weight_decay: 0.01
learning rate: 1e-4
epoch: 20
batch size: 64
precision: 16-mixed
- Library
- peft
- Created
- Jul 17, 2024
- Updated
- Oct 16, 2024