Protein Model Training Workflow

①Data Preparation

Data Cleaning

Filter invalid sequences or labels.

Normalization

Scale labels (e.g., T_m values) to a standard range.

②Iterative Training Loop

The model learns by repeatedly processing the data in small batches. This entire loop is performed many times (across "epochs").

For each batch (e.g., 2 sequences):

Tokenize Protein Sequences
↓
Forward Pass → Generate Embeddings & Prediction
↓
Calculate Loss (Prediction vs. True Label)
↓
Backward Pass (Calculate Gradients)
↓
Update Model Weights (Optimizer Step)

③Final Training Outputs

Updated LoRA Weights

A small set of parameters representing the changes made to the original model's target layers.

Trained Regression Head

A small neural network that converts the final protein embedding into a specific value (e.g., T_m).

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