Update README.md

README.md

@@ -1,370 +1,373 @@
----
-license: cc-by-nc-nd-4.0
----
-
-This repo contains important large files for [PeptiVerse](https://huggingface.co/spaces/ChatterjeeLab/PeptiVerse), an interactive app for peptide property prediction. 
-
-- `embeddings` folder contains processed huggingface datasets with peptideCLM embeddings. The `.csv` is the pre-processed data.
-- `metrics` folder contains the model performance on the validation data
-- `models` host all trained model weights
-- `training_data` host all **raw data** to train the classifiers
-- `functions` contains files to utilize the trained weights and classifiers
-- `train` contains the script to train classifiers on the pre-processed embeddings, either through xgboost or MLPs.
-- `scoring_function.py` contains a class that aggregates all trained classifiers for diverse downstream sampling applications
-
-# PeptiVerse 🧬🌌
-
-A collection of machine learning predictors for non-canonical and canonical peptide property prediction for SMILES representation. 🧬 PeptiVerse 🌌 enables evaluation of key biophysical and therapeutic properties of peptides for property-optimized generation.
-
-## Predictors 🧫
-
-PeptiVerse includes the following property predictors:
-
-| Predictor | Measurement | Interpretation | Training Data Source | Dataset Size | Model Type |
-|-----------|-------------|-----------------| --------------------|--------------|------------|
-| **Non-Hemolysis** | Probability of non-hemolytic behavior | 0-1 scale, higher = less hemolytic | PeptideBERT, PepLand | 6,077 peptides | XGBoost + PeptideCLM embeddings |
-| **Solubility** | Probability of aqueous solubility | 0-1 scale, higher = more soluble | PeptideBERT, PepLand | 18,454 peptides | XGBoost + PeptideCLM embeddings |
-| **Non-Fouling** | Probability of non-fouling properties | 0-1 scale, higher = lower probability of binding to off-targets | PeptideBERT, PepLand | 17,186 peptides | XGBoost + PeptideCLM embeddings |
-| **Permeability** | Cell membrane permeability (PAMPA lipophilicity score log P scale, range -10 to 0) | ≥ −6.0 indicate strong permeability and values < 6.0 indicate weak permeability | ChEMBL (22,040), CycPeptMPDB (7451) | 34,853 peptides | XGBoost + PeptideCLM embeddings + molecular descriptors |
-| **Binding Affinity** | Peptide-protein binding strength (-log Kd/Ki/IC50 scale) | Weak binding (< 6.0), medium binding (6.0 − 7.5), and high binding (≥ 7.5) | PepLand | 1806 peptide-protein pairs | Cross-attention transformer (ESM2 + PeptideCLM) |
-
-## Model Performance 🌟
-
-#### Binary Classification Predictors
-
-| Predictor | Val AUC | Val F1 |
-|-----------|----------------|----------|
-| **Non-Hemolysis** | 0.7902 | 0.8260 |
-| **Solubility** | 0.6016 | 0.5767 |
-| **Nonfouling** | 0.9327 | 0.8774 |
-
-#### Regression Predictors
-
-| Predictor | Train Correlation (Spearman) | Val Correlation (Spearman) |
-|-----------|------------------------------|----------------------------|
-| **Permeability** | 0.958 | 0.710 |
-| **Binding Affinity** | 0.805 | 0.611 |
-
-## Setup 🌟
-
-1. Clone the repository:
-```bash
-git clone https://github.com/sophtang/PeptiVerse.git
-cd PeptiVerse
-```
-
-2. Install environment:
-```bash
-conda env create -f environment.yml
-
-conda activate peptiverse
-```
-
-3. Change the `base_path` in each file to ensure that all model weights and tokenizers are loaded correctly.
-
-## Usage 🌟
-
-#### 1. Hemolysis Prediction
-
-Predicts the probability that a peptide is **not hemolytic**. Higher scores indicate safer peptides.
-
-```python
-import sys
-sys.path.append('/path/to/PeptiVerse')
-from functions.hemolysis.hemolysis import Hemolysis
-
-# Initialize predictor
-hemo = Hemolysis()
-
-# Input peptide in SMILES format
-peptides = [
-    "NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC1=CN=C-N1)C(=O)O"
-]
-
-# Get predictions
-scores = hemo(peptides)
-print(f"Non-hemolytic probability: {scores[0]:.3f}")
-```
-
-**Output interpretation:**
-- Score close to 1.0 = likely non-hemolytic (safe)
-- Score close to 0.0 = likely hemolytic (unsafe)
-
----
-
-#### 2. Solubility Prediction
-
-Predicts aqueous solubility. Higher scores indicate better solubility.
-
-```python
-from functions.solubility.solubility import Solubility
-
-# Initialize predictor
-sol = Solubility()
-
-# Input peptide
-peptides = [
-    "NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC1=CN=C-N1)C(=O)O"
-]
-
-# Get predictions
-scores = sol(peptides)
-print(f"Solubility probability: {scores[0]:.3f}")
-```
-
-**Output interpretation:**
-- Score close to 1.0 = highly soluble
-- Score close to 0.0 = poorly soluble
-
----
-
-#### 3. Nonfouling Prediction
-
-Predicts protein resistance/non-fouling properties.
-
-```python
-from functions.nonfouling.nonfouling import Nonfouling
-
-# Initialize predictor
-nf = Nonfouling()
-
-# Input peptide
-peptides = [
-    "NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC1=CN=C-N1)C(=O)O"
-]
-
-# Get predictions
-scores = nf(peptides)
-print(f"Nonfouling score: {scores[0]:.3f}")
-```
-
-**Output interpretation:**
-- Higher scores = better non-fouling properties
-
----
-
-#### 4. Permeability Prediction
-
-Predicts membrane permeability on a log P scale.
-
-```python
-from functions.permeability.permeability import Permeability
-
-# Initialize predictor
-perm = Permeability()
-
-# Input peptide
-peptides = [
-    "N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](Cc1cNc2c1cc(O)cc2)C(=O)O"
-]
-
-# Get predictions
-scores = perm(peptides)
-print(f"Permeability (log P): {scores[0]:.3f}")
-```
-
-**Output interpretation:**
-- Higher values = more permeable
-- Typical range: -10 to 0 (log scale)
-
----
-
-#### 5. Binding Affinity Prediction
-
-Predicts peptide-protein binding affinity. Requires both peptide and target protein sequence.
-
-```python
-from functions.binding.binding import BindingAffinity
-
-# Target protein sequence (amino acid format)
-target_protein = "MTKSNGEEPKMGGRMERFQQGVRKRTLLAKKKVQNITKEDVKSYLFRNAFVLL..."
-
-# Initialize predictor with target protein
-binding = BindingAffinity(prot_seq=target_protein)
-
-# Input peptide in SMILES format
-peptides = [
-    "CC[C@H](C)[C@H](NC(=O)[C@H](C)NC(=O)[C@@H](N)Cc1c[nH]cn1)C(=O)O"
-]
-
-# Get predictions
-scores = binding(peptides)
-print(f"Binding affinity (-log Kd): {scores[0]:.3f}")
-```
-
-**Output interpretation:**
-- Higher values = stronger binding
-- Scale: -log(Kd/Ki/IC50)
-  - 7.5+ = tight binding (≤ ~30nM)
-  - 6.0-7.5 = medium binding (~30nM - 1μM)
-  - <6.0 = weak binding (> 1μM)
-
----
-
-## Batch Processing 🌟
-
-All predictors support batch processing for multiple peptides:
-
-```python
-from functions.hemolysis.hemolysis import Hemolysis
-
-hemo = Hemolysis()
-
-# Multiple peptides
-peptides = [
-    "NCC(=O)N[C@H](CS)C(=O)O",
-    "CC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(=O)O)C(=O)O",
-    "N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)O"
-]
-
-# Get predictions for all
-scores = hemo(peptides)
-
-for i, score in enumerate(scores):
-    print(f"Peptide {i+1}: {score:.3f}")
-```
-
----
-
-## Unified Scoring with Multiple Predictors 🌟
-
-For convenience, you can use `scoring_functions.py` to evaluate multiple properties at once and get a score vector for each peptide.
-
-### Basic Usage
-
-```python
-import sys
-sys.path.append('/path/to/PeptiVerse')
-from scoring_functions import ScoringFunctions
-
-# Initialize with desired scoring functions
-# Available: 'binding_affinity1', 'binding_affinity2', 'permeability', 
-#            'solubility', 'hemolysis', 'nonfouling'
-scoring = ScoringFunctions(
-    score_func_names=['solubility', 'hemolysis', 'nonfouling', 'permeability'],
-    prot_seqs=[]  # Empty if not using binding affinity
-)
-
-# Input peptides in SMILES format
-peptides = [
-    'N2[C@H](CC(C)C)C(=O)N1[C@@H](CCC1)C(=O)N[C@@H](Cc1ccccc1)C2(=O)',
-    'NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)O'
-]
-
-# Get scores (returns numpy array of shape: num_peptides x num_functions)
-scores = scoring(input_seqs=peptides)
-print(scores)
-```
-
-### Adding Binding Affinity
-
-```python
-from scoring_functions import ScoringFunctions
-
-# Target protein sequence (amino acid format)
-tfr_protein = "MMDQARSAFSNLFGGEPLSYTRFSLARQVDGDNSHVEMKLAVDEEENADNNT..."
-
-# Initialize with binding affinity for one protein
-scoring = ScoringFunctions(
-    score_func_names=['binding_affinity1', 'solubility', 'hemolysis', 'permeability'],
-    prot_seqs=[tfr_protein]  # Provide target protein sequence
-)
-
-peptides = ['N2[C@H](CC(C)C)C(=O)N1[C@@H](CCC1)C(=O)N[C@@H](Cc1ccccc1)C2(=O)']
-scores = scoring(input_seqs=peptides)
-
-# scores[0] will contain: [binding_affinity, solubility, hemolysis, permeability]
-print(f"Scores for peptide 1:")
-print(f"  Binding Affinity: {scores[0][0]:.3f}")
-print(f"  Solubility: {scores[0][1]:.3f}")
-print(f"  Hemolysis: {scores[0][2]:.3f}")
-print(f"  Permeability: {scores[0][3]:.3f}")
-```
-
-### Multiple Binding Targets
-
-```python
-# For dual binding affinity prediction
-protein1 = "MMDQARSAFSNLFGGEPLSYTR..."  # First target
-protein2 = "MTKSNGEEPKMGGRMERFQQGV..."  # Second target
-
-scoring = ScoringFunctions(
-    score_func_names=['binding_affinity1', 'binding_affinity2', 'solubility', 'hemolysis'],
-    prot_seqs=[protein1, protein2]  # Provide both protein sequences
-)
-
-peptides = ['N2[C@H](CC(C)C)C(=O)N1[C@@H](CCC1)C(=O)...']
-scores = scoring(input_seqs=peptides)
-
-# scores[0] will contain: [binding_aff1, binding_aff2, solubility, hemolysis]
-```
-
-### Output Format
-
-The `ScoringFunctions` class returns a numpy array where:
-- **Rows**: Each row corresponds to one input peptide
-- **Columns**: Each column corresponds to one scoring function (in the order specified)
-
-```python
-# Example with 3 peptides and 4 scoring functions
-scores = scoring(input_seqs=peptides)  
-# Shape: (3, 4)
-# scores[0] = [func1_score, func2_score, func3_score, func4_score] for peptide 1
-# scores[1] = [func1_score, func2_score, func3_score, func4_score] for peptide 2
-# scores[2] = [func1_score, func2_score, func3_score, func4_score] for peptide 3
-```
-
----
-
-## Complete Example 🌟
-
-```python
-import sys
-sys.path.append('/path/to/PeptiVerse')
-from functions.hemolysis.hemolysis import Hemolysis
-from functions.solubility.solubility import Solubility
-from functions.permeability.permeability import Permeability
-
-# Initialize predictors
-hemo = Hemolysis()
-sol = Solubility()
-perm = Permeability()
-
-# Test peptide
-peptide = ["NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)O"]
-
-# Get all predictions
-hemo_score = hemo(peptide)[0]
-sol_score = sol(peptide)[0]
-perm_score = perm(peptide)[0]
-
-print("Peptide Property Predictions:")
-print(f"  Hemolysis (non-hemolytic prob): {hemo_score:.3f}")
-print(f"  Solubility: {sol_score:.3f}")
-print(f"  Permeability: {perm_score:.3f}")
-```
-
----
-
-## Model Architecture 🌟
-
-All predictors use:
-- **Embeddings**: PeptideCLM-23M (RoFormer-based peptide language model)
-- **Classifier**: XGBoost gradient boosting
-- **Input**: SMILES representation of peptides
-- **Training**: Models trained on curated datasets with cross-validation
-
----
-## Citation
-
-If you find this repository helpful for your publications, please consider citing our paper:
-
-```
-@article{tang2025peptune,
-  title={Peptune: De novo generation of therapeutic peptides with multi-objective-guided discrete diffusion},
-  author={Tang, Sophia and Zhang, Yinuo and Chatterjee, Pranam},
-  journal={42nd International Conference on Machine Learning},
-  year={2025}
-}
-```
+---
+license: cc-by-nc-nd-4.0
+---
+
+
+![Untitled design (3)](https://cdn-uploads.huggingface.co/production/uploads/64cd5b3f0494187a9e8b7c69/bpOe1xggl9lw90JMi3VsC.png)
+
+This repo contains important large files for [PeptiVerse](https://huggingface.co/spaces/ChatterjeeLab/PeptiVerse), an interactive app for peptide property prediction. 
+
+- `embeddings` folder contains processed huggingface datasets with PeptideCLM embeddings. The `.csv` is the pre-processed data.
+- `metrics` folder contains the model performance on the validation data
+- `models` host all trained model weights
+- `training_data` host all **raw data** to train the classifiers
+- `functions` contains files to utilize the trained weights and classifiers
+- `train` contains the script to train classifiers on the pre-processed embeddings, either through xgboost or MLPs.
+- `scoring_function.py` contains a class that aggregates all trained classifiers for diverse downstream sampling applications
+
+# PeptiVerse 🧬🌌
+
+A collection of machine learning predictors for non-canonical and canonical peptide property prediction for SMILES representation. 🧬 PeptiVerse 🌌 enables evaluation of key biophysical and therapeutic properties of peptides for property-optimized generation.
+
+## Predictors 🧫
+
+PeptiVerse includes the following property predictors:
+
+| Predictor | Measurement | Interpretation | Training Data Source | Dataset Size | Model Type |
+|-----------|-------------|-----------------| --------------------|--------------|------------|
+| **Non-Hemolysis** | Probability of non-hemolytic behavior | 0-1 scale, higher = less hemolytic | PeptideBERT, PepLand | 6,077 peptides | XGBoost + PeptideCLM embeddings |
+| **Solubility** | Probability of aqueous solubility | 0-1 scale, higher = more soluble | PeptideBERT, PepLand | 18,454 peptides | XGBoost + PeptideCLM embeddings |
+| **Non-Fouling** | Probability of non-fouling properties | 0-1 scale, higher = lower probability of binding to off-targets | PeptideBERT, PepLand | 17,186 peptides | XGBoost + PeptideCLM embeddings |
+| **Permeability** | Cell membrane permeability (PAMPA lipophilicity score log P scale, range -10 to 0) | ≥ −6.0 indicate strong permeability and values < 6.0 indicate weak permeability | ChEMBL (22,040), CycPeptMPDB (7451) | 34,853 peptides | XGBoost + PeptideCLM embeddings + molecular descriptors |
+| **Binding Affinity** | Peptide-protein binding strength (-log Kd/Ki/IC50 scale) | Weak binding (< 6.0), medium binding (6.0 − 7.5), and high binding (≥ 7.5) | PepLand | 1806 peptide-protein pairs | Cross-attention transformer (ESM2 + PeptideCLM) |
+
+## Model Performance 🌟
+
+#### Binary Classification Predictors
+
+| Predictor | Val AUC | Val F1 |
+|-----------|----------------|----------|
+| **Non-Hemolysis** | 0.7902 | 0.8260 |
+| **Solubility** | 0.6016 | 0.5767 |
+| **Nonfouling** | 0.9327 | 0.8774 |
+
+#### Regression Predictors
+
+| Predictor | Train Correlation (Spearman) | Val Correlation (Spearman) |
+|-----------|------------------------------|----------------------------|
+| **Permeability** | 0.958 | 0.710 |
+| **Binding Affinity** | 0.805 | 0.611 |
+
+## Setup 🌟
+
+1. Clone the repository:
+```bash
+git clone https://github.com/sophtang/PeptiVerse.git
+cd PeptiVerse
+```
+
+2. Install environment:
+```bash
+conda env create -f environment.yml
+
+conda activate peptiverse
+```
+
+3. Change the `base_path` in each file to ensure that all model weights and tokenizers are loaded correctly.
+
+## Usage 🌟
+
+#### 1. Hemolysis Prediction
+
+Predicts the probability that a peptide is **not hemolytic**. Higher scores indicate safer peptides.
+
+```python
+import sys
+sys.path.append('/path/to/PeptiVerse')
+from functions.hemolysis.hemolysis import Hemolysis
+
+# Initialize predictor
+hemo = Hemolysis()
+
+# Input peptide in SMILES format
+peptides = [
+    "NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC1=CN=C-N1)C(=O)O"
+]
+
+# Get predictions
+scores = hemo(peptides)
+print(f"Non-hemolytic probability: {scores[0]:.3f}")
+```
+
+**Output interpretation:**
+- Score close to 1.0 = likely non-hemolytic (safe)
+- Score close to 0.0 = likely hemolytic (unsafe)
+
+---
+
+#### 2. Solubility Prediction
+
+Predicts aqueous solubility. Higher scores indicate better solubility.
+
+```python
+from functions.solubility.solubility import Solubility
+
+# Initialize predictor
+sol = Solubility()
+
+# Input peptide
+peptides = [
+    "NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC1=CN=C-N1)C(=O)O"
+]
+
+# Get predictions
+scores = sol(peptides)
+print(f"Solubility probability: {scores[0]:.3f}")
+```
+
+**Output interpretation:**
+- Score close to 1.0 = highly soluble
+- Score close to 0.0 = poorly soluble
+
+---
+
+#### 3. Nonfouling Prediction
+
+Predicts protein resistance/non-fouling properties.
+
+```python
+from functions.nonfouling.nonfouling import Nonfouling
+
+# Initialize predictor
+nf = Nonfouling()
+
+# Input peptide
+peptides = [
+    "NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC1=CN=C-N1)C(=O)O"
+]
+
+# Get predictions
+scores = nf(peptides)
+print(f"Nonfouling score: {scores[0]:.3f}")
+```
+
+**Output interpretation:**
+- Higher scores = better non-fouling properties
+
+---
+
+#### 4. Permeability Prediction
+
+Predicts membrane permeability on a log P scale.
+
+```python
+from functions.permeability.permeability import Permeability
+
+# Initialize predictor
+perm = Permeability()
+
+# Input peptide
+peptides = [
+    "N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](Cc1cNc2c1cc(O)cc2)C(=O)O"
+]
+
+# Get predictions
+scores = perm(peptides)
+print(f"Permeability (log P): {scores[0]:.3f}")
+```
+
+**Output interpretation:**
+- Higher values = more permeable
+- Typical range: -10 to 0 (log scale)
+
+---
+
+#### 5. Binding Affinity Prediction
+
+Predicts peptide-protein binding affinity. Requires both peptide and target protein sequence.
+
+```python
+from functions.binding.binding import BindingAffinity
+
+# Target protein sequence (amino acid format)
+target_protein = "MTKSNGEEPKMGGRMERFQQGVRKRTLLAKKKVQNITKEDVKSYLFRNAFVLL..."
+
+# Initialize predictor with target protein
+binding = BindingAffinity(prot_seq=target_protein)
+
+# Input peptide in SMILES format
+peptides = [
+    "CC[C@H](C)[C@H](NC(=O)[C@H](C)NC(=O)[C@@H](N)Cc1c[nH]cn1)C(=O)O"
+]
+
+# Get predictions
+scores = binding(peptides)
+print(f"Binding affinity (-log Kd): {scores[0]:.3f}")
+```
+
+**Output interpretation:**
+- Higher values = stronger binding
+- Scale: -log(Kd/Ki/IC50)
+  - 7.5+ = tight binding (≤ ~30nM)
+  - 6.0-7.5 = medium binding (~30nM - 1μM)
+  - <6.0 = weak binding (> 1μM)
+
+---
+
+## Batch Processing 🌟
+
+All predictors support batch processing for multiple peptides:
+
+```python
+from functions.hemolysis.hemolysis import Hemolysis
+
+hemo = Hemolysis()
+
+# Multiple peptides
+peptides = [
+    "NCC(=O)N[C@H](CS)C(=O)O",
+    "CC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(=O)O)C(=O)O",
+    "N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)O"
+]
+
+# Get predictions for all
+scores = hemo(peptides)
+
+for i, score in enumerate(scores):
+    print(f"Peptide {i+1}: {score:.3f}")
+```
+
+---
+
+## Unified Scoring with Multiple Predictors 🌟
+
+For convenience, you can use `scoring_functions.py` to evaluate multiple properties at once and get a score vector for each peptide.
+
+### Basic Usage
+
+```python
+import sys
+sys.path.append('/path/to/PeptiVerse')
+from scoring_functions import ScoringFunctions
+
+# Initialize with desired scoring functions
+# Available: 'binding_affinity1', 'binding_affinity2', 'permeability', 
+#            'solubility', 'hemolysis', 'nonfouling'
+scoring = ScoringFunctions(
+    score_func_names=['solubility', 'hemolysis', 'nonfouling', 'permeability'],
+    prot_seqs=[]  # Empty if not using binding affinity
+)
+
+# Input peptides in SMILES format
+peptides = [
+    'N2[C@H](CC(C)C)C(=O)N1[C@@H](CCC1)C(=O)N[C@@H](Cc1ccccc1)C2(=O)',
+    'NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)O'
+]
+
+# Get scores (returns numpy array of shape: num_peptides x num_functions)
+scores = scoring(input_seqs=peptides)
+print(scores)
+```
+
+### Adding Binding Affinity
+
+```python
+from scoring_functions import ScoringFunctions
+
+# Target protein sequence (amino acid format)
+tfr_protein = "MMDQARSAFSNLFGGEPLSYTRFSLARQVDGDNSHVEMKLAVDEEENADNNT..."
+
+# Initialize with binding affinity for one protein
+scoring = ScoringFunctions(
+    score_func_names=['binding_affinity1', 'solubility', 'hemolysis', 'permeability'],
+    prot_seqs=[tfr_protein]  # Provide target protein sequence
+)
+
+peptides = ['N2[C@H](CC(C)C)C(=O)N1[C@@H](CCC1)C(=O)N[C@@H](Cc1ccccc1)C2(=O)']
+scores = scoring(input_seqs=peptides)
+
+# scores[0] will contain: [binding_affinity, solubility, hemolysis, permeability]
+print(f"Scores for peptide 1:")
+print(f"  Binding Affinity: {scores[0][0]:.3f}")
+print(f"  Solubility: {scores[0][1]:.3f}")
+print(f"  Hemolysis: {scores[0][2]:.3f}")
+print(f"  Permeability: {scores[0][3]:.3f}")
+```
+
+### Multiple Binding Targets
+
+```python
+# For dual binding affinity prediction
+protein1 = "MMDQARSAFSNLFGGEPLSYTR..."  # First target
+protein2 = "MTKSNGEEPKMGGRMERFQQGV..."  # Second target
+
+scoring = ScoringFunctions(
+    score_func_names=['binding_affinity1', 'binding_affinity2', 'solubility', 'hemolysis'],
+    prot_seqs=[protein1, protein2]  # Provide both protein sequences
+)
+
+peptides = ['N2[C@H](CC(C)C)C(=O)N1[C@@H](CCC1)C(=O)...']
+scores = scoring(input_seqs=peptides)
+
+# scores[0] will contain: [binding_aff1, binding_aff2, solubility, hemolysis]
+```
+
+### Output Format
+
+The `ScoringFunctions` class returns a numpy array where:
+- **Rows**: Each row corresponds to one input peptide
+- **Columns**: Each column corresponds to one scoring function (in the order specified)
+
+```python
+# Example with 3 peptides and 4 scoring functions
+scores = scoring(input_seqs=peptides)  
+# Shape: (3, 4)
+# scores[0] = [func1_score, func2_score, func3_score, func4_score] for peptide 1
+# scores[1] = [func1_score, func2_score, func3_score, func4_score] for peptide 2
+# scores[2] = [func1_score, func2_score, func3_score, func4_score] for peptide 3
+```
+
+---
+
+## Complete Example 🌟
+
+```python
+import sys
+sys.path.append('/path/to/PeptiVerse')
+from functions.hemolysis.hemolysis import Hemolysis
+from functions.solubility.solubility import Solubility
+from functions.permeability.permeability import Permeability
+
+# Initialize predictors
+hemo = Hemolysis()
+sol = Solubility()
+perm = Permeability()
+
+# Test peptide
+peptide = ["NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)O"]
+
+# Get all predictions
+hemo_score = hemo(peptide)[0]
+sol_score = sol(peptide)[0]
+perm_score = perm(peptide)[0]
+
+print("Peptide Property Predictions:")
+print(f"  Hemolysis (non-hemolytic prob): {hemo_score:.3f}")
+print(f"  Solubility: {sol_score:.3f}")
+print(f"  Permeability: {perm_score:.3f}")
+```
+
+---
+
+## Model Architecture 🌟
+
+All predictors use:
+- **Embeddings**: PeptideCLM-23M (RoFormer-based peptide language model)
+- **Classifier**: XGBoost gradient boosting
+- **Input**: SMILES representation of peptides
+- **Training**: Models trained on curated datasets with cross-validation
+
+---
+## Citation
+
+If you find this repository helpful for your publications, please consider citing our paper:
+
+```
+@article{tang2025peptune,
+  title={Peptune: De novo generation of therapeutic peptides with multi-objective-guided discrete diffusion},
+  author={Tang, Sophia and Zhang, Yinuo and Chatterjee, Pranam},
+  journal={42nd International Conference on Machine Learning},
+  year={2025}
+}
+```
 To use this repository, you agree to abide by the MIT License.