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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+# Nanobody (VHH) Affinity Prediction Dataset
+
+## Dataset Overview
+
+This dataset helps predict the binding affinity between nanobodies (VHH, single-domain antibodies from camelids) and their target antigens. Affinity is a key parameter that measures how strongly an antibody binds to its antigen, usually expressed as dissociation constant (KD) or binding free energy.
+
+High affinity is a critical property for therapeutic antibodies, so accurately predicting nanobody affinity is important for antibody engineering and screening.
+
+## Data Collection
+
+The dataset is based on experimentally measured nanobody-antigen binding affinities. Data is collected from published literature and split based on score (stratified split)
+
+## Dataset Structure
+
+The dataset is split into training, validation, and test sets.
+
+### File Format
+
+CSV files contain these columns:
+- `seq`: Nanobody amino acid sequence
+- `score`: Affinity value (typically -log10(KD) where KD is in M), higher values indicate stronger binding affinity
+
+## Uses and Limitations
+
+### Uses
+- Develop models to predict nanobody affinity
+- Help select and optimize nanobodies
+- Reduce experimental work and speed up drug development
+
+### Limitations
+- Differences in affinity measurement methods may cause data variability
+- The same antibody-antigen pair may have different affinity values under different conditions
+- The dataset may not cover all possible nanobody-antigen combinations
+
+## Evaluation Metrics
+
+Model performance is evaluated using:
+- Spearman correlation
+- R²
+- Root Mean Squared Error (RMSE)
+- Mean Absolute Error (MAE)