IonNTxPred

🧠 IonNTxPred: LLM-based Prediction and Designing of Ion Channel Impairing Proteins

IonNTxPred is a fine-tuned transformer model built on top of the esm2_t33_650M_UR50D protein language model. It is specifically trained for binary classification of peptide sequences — predicting whether a peptide is ion channel modulating or non-modulating.

🎯 Use Case: Accelerating the identification and design of safe peptide therapeutics by filtering out ion channel impairing/modulating candidates early in the drug development pipeline.

---

🖼️ IonNTxPred Workflow

---

🧬 Model Highlights

• Base Model: Facebook’s ESM2-t33 (650M parameters)
• Fine-Tuning Task: Ion channel toxins prediction (binary classification)
• Input: Protein/Peptide sequences
• Output: Binary label → 1 (Ion Channel Modulating), 0 (non-modulating)
• Architecture: ESM2 encoder + linear classification head

---

🗂️ Files Included

• config.json – Contains configuration settings for the model architecture, hyperparameters, and training details.

• model.safetensors – This is the actual trained model weights saved in the SafeTensors format, which is safer and faster than the traditional .bin files.

• special_tokens_map.json – Stores mappings for special tokens, like [CLS], [SEP], or any custom tokens used in your tokenizer.

• tokenizer_config.json – Contains tokenizer-related settings (like vocabulary size, tokenization method).

• vocab.txt – Lists all tokens and their corresponding IDs; it's essential for text tokenization.

---

🚀 How to Use

---

Predict Sodium channel modulating proteins

🔧 Install Dependencies

pip install torch esm biopython huggingface_hub


### Loading the Model from Hugging Face

```python
import torch
import esm
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from transformers import AutoTokenizer, EsmForSequenceClassification

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


print("Downloading fine-tuned models & weights...")
repo_id = "anandr88/IonNTxPred"
subfolder = "saved_model_t33_na"
# Load the tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = EsmForSequenceClassification.from_pretrained(repo_id, subfolder=subfolder)
weights_path = hf_hub_download(repo_id=repo_id, filename="saved_model_t33_na/model.safetensors")

# Create a simple classifier model
class ProteinClassifier(torch.nn.Module):
    def __init__(self, esm_model):
        super().__init__()
        self.esm_model = esm_model
        # We'll dynamically determine the classifier layer size
        self.classifier = None
    
    def forward(self, tokens):
        with torch.no_grad():
            results = self.esm_model(tokens, repr_layers=[33], return_contacts=False)
        embeddings = results["representations"][33].mean(1)
        return self.classifier(embeddings)

# Initialize model
classifier = ProteinClassifier(model)

# Load the state dict and determine architecture
state_dict = load_file(weights_path, device=str(device))

# Find the classifier layer (look for a weight matrix)
for key, tensor in state_dict.items():
    if len(tensor.shape) == 2:  # This should be the weight matrix
        num_classes = tensor.shape[0]
        embedding_dim = tensor.shape[1]
        print(f"Found classifier layer: {key} (input_dim={embedding_dim}, output_dim={num_classes})")
        
        # Initialize the classifier layer
        classifier.classifier = torch.nn.Linear(embedding_dim, num_classes).to(device)
        
        # Create new state dict with proper names
        new_state_dict = {
            'classifier.weight': state_dict[key],
            'classifier.bias': state_dict[key.replace('weight', 'bias')]
        }
        classifier.load_state_dict(new_state_dict, strict=False)
        break

# Move to device and set to eval mode
classifier = classifier.to(device)
classifier.eval()

print(f"\nModel successfully loaded on {device} and ready for inference!")

🧪 Example Usage (Optional)

from transformers import AutoTokenizer, EsmForSequenceClassification
import torch

# Define the repository ID and subfolder
repo_id = "anandr88/IonNTxPred"
subfolder = "saved_model_t33_na"

# Load the tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = EsmForSequenceClassification.from_pretrained(repo_id, subfolder=subfolder)

# Move the model to the appropriate device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Function to make predictions
def make_predictions(model, inputs, device):
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.softmax(logits, dim=1)[:, 1].cpu().numpy()
    return probs

# Example protein sequence
protein_sequence = "MKASTLVVIFIVIFITISSFSIHDVQASGVEKREQKDCLKKLKLCKENKDCCSKSCKRRGTNIEKRCR"

# Tokenize the input sequence
inputs = tokenizer(protein_sequence, return_tensors="pt", truncation=True, padding=True)
inputs = {key: value.to(device) for key, value in inputs.items()}

# Make predictions
prediction = make_predictions(model, inputs, device)

# Apply threshold for final classification
threshold = 0.5
final_prediction = "Na+ channel modulating" if prediction[0] > threshold else "Not Na+ channel modulating"

print(f"📊 Prediction Probability: {prediction[0]:.4f}")
print(f"🏷️ Final Prediction: {final_prediction}")

---

Predict Potassium channel modulating proteins

---

🔧 Install Dependencies

pip install torch esm biopython huggingface_hub


### Loading the Model from Hugging Face

```python
import torch
import esm
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from transformers import AutoTokenizer, EsmForSequenceClassification

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


print("Downloading fine-tuned models & weights...")
repo_id = "anandr88/IonNTxPred"
subfolder = "saved_model_t33_k"
# Load the tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = EsmForSequenceClassification.from_pretrained(repo_id, subfolder=subfolder)
weights_path = hf_hub_download(repo_id=repo_id, filename="saved_model_t33_na/model.safetensors")

# Create a simple classifier model
class ProteinClassifier(torch.nn.Module):
    def __init__(self, esm_model):
        super().__init__()
        self.esm_model = esm_model
        # We'll dynamically determine the classifier layer size
        self.classifier = None
    
    def forward(self, tokens):
        with torch.no_grad():
            results = self.esm_model(tokens, repr_layers=[33], return_contacts=False)
        embeddings = results["representations"][33].mean(1)
        return self.classifier(embeddings)

# Initialize model
classifier = ProteinClassifier(model)

# Load the state dict and determine architecture
state_dict = load_file(weights_path, device=str(device))

# Find the classifier layer (look for a weight matrix)
for key, tensor in state_dict.items():
    if len(tensor.shape) == 2:  # This should be the weight matrix
        num_classes = tensor.shape[0]
        embedding_dim = tensor.shape[1]
        print(f"Found classifier layer: {key} (input_dim={embedding_dim}, output_dim={num_classes})")
        
        # Initialize the classifier layer
        classifier.classifier = torch.nn.Linear(embedding_dim, num_classes).to(device)
        
        # Create new state dict with proper names
        new_state_dict = {
            'classifier.weight': state_dict[key],
            'classifier.bias': state_dict[key.replace('weight', 'bias')]
        }
        classifier.load_state_dict(new_state_dict, strict=False)
        break

# Move to device and set to eval mode
classifier = classifier.to(device)
classifier.eval()

print(f"\nModel successfully loaded on {device} and ready for inference!")

🧪 Example Usage (Optional)

from transformers import AutoTokenizer, EsmForSequenceClassification
import torch

# Define the repository ID and subfolder
repo_id = "anandr88/IonNTxPred"
subfolder = "saved_model_t33_k"

# Load the tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = EsmForSequenceClassification.from_pretrained(repo_id, subfolder=subfolder)

# Move the model to the appropriate device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Function to make predictions
def make_predictions(model, inputs, device):
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.softmax(logits, dim=1)[:, 1].cpu().numpy()
    return probs

# Example protein sequence
protein_sequence = "MKASTLVVIFIVIFITISSFSIHDVQASGVEKREQKDCLKKLKLCKENKDCCSKSCKRRGTNIEKRCR"

# Tokenize the input sequence
inputs = tokenizer(protein_sequence, return_tensors="pt", truncation=True, padding=True)
inputs = {key: value.to(device) for key, value in inputs.items()}

# Make predictions
prediction = make_predictions(model, inputs, device)

# Apply threshold for final classification
threshold = 0.5
final_prediction = "K+ channel modulating" if prediction[0] > threshold else "Not K+ channel modulating"

print(f"📊 Prediction Probability: {prediction[0]:.4f}")
print(f"🏷️ Final Prediction: {final_prediction}")

---

Predict Calcilum channel modulating proteins

---

🔧 Install Dependencies

pip install torch esm biopython huggingface_hub


### Loading the Model from Hugging Face

```python
import torch
import esm
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from transformers import AutoTokenizer, EsmForSequenceClassification

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


print("Downloading fine-tuned models & weights...")
repo_id = "anandr88/IonNTxPred"
subfolder = "saved_model_t33_ca"
# Load the tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = EsmForSequenceClassification.from_pretrained(repo_id, subfolder=subfolder)
weights_path = hf_hub_download(repo_id=repo_id, filename="saved_model_t33_ca/model.safetensors")

# Create a simple classifier model
class ProteinClassifier(torch.nn.Module):
    def __init__(self, esm_model):
        super().__init__()
        self.esm_model = esm_model
        # We'll dynamically determine the classifier layer size
        self.classifier = None
    
    def forward(self, tokens):
        with torch.no_grad():
            results = self.esm_model(tokens, repr_layers=[33], return_contacts=False)
        embeddings = results["representations"][33].mean(1)
        return self.classifier(embeddings)

# 4. Initialize model
classifier = ProteinClassifier(model)

# 5. Load the state dict and determine architecture
state_dict = load_file(weights_path, device=str(device))

# Find the classifier layer (look for a weight matrix)
for key, tensor in state_dict.items():
    if len(tensor.shape) == 2:  # This should be the weight matrix
        num_classes = tensor.shape[0]
        embedding_dim = tensor.shape[1]
        print(f"Found classifier layer: {key} (input_dim={embedding_dim}, output_dim={num_classes})")
        
        # Initialize the classifier layer
        classifier.classifier = torch.nn.Linear(embedding_dim, num_classes).to(device)
        
        # Create new state dict with proper names
        new_state_dict = {
            'classifier.weight': state_dict[key],
            'classifier.bias': state_dict[key.replace('weight', 'bias')]
        }
        classifier.load_state_dict(new_state_dict, strict=False)
        break

# Move to device and set to eval mode
classifier = classifier.to(device)
classifier.eval()

print(f"\nModel successfully loaded on {device} and ready for inference!")

🧪 Example Usage (Optional)

from transformers import AutoTokenizer, EsmForSequenceClassification
import torch

# Define the repository ID and subfolder
repo_id = "anandr88/IonNTxPred"
subfolder = "saved_model_t33_ca"

# Load the tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = EsmForSequenceClassification.from_pretrained(repo_id, subfolder=subfolder)

# Move the model to the appropriate device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Function to make predictions
def make_predictions(model, inputs, device):
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.softmax(logits, dim=1)[:, 1].cpu().numpy()
    return probs

# Example protein sequence
protein_sequence = "MKASTLVVIFIVIFITISSFSIHDVQASGVEKREQKDCLKKLKLCKENKDCCSKSCKRRGTNIEKRCR"

# Tokenize the input sequence
inputs = tokenizer(protein_sequence, return_tensors="pt", truncation=True, padding=True)
inputs = {key: value.to(device) for key, value in inputs.items()}

# Make predictions
prediction = make_predictions(model, inputs, device)

# Apply threshold for final classification
threshold = 0.5
final_prediction = "Ca++ channel modulating" if prediction[0] > threshold else "Not Ca++ channel modulating"

print(f"📊 Prediction Probability: {prediction[0]:.4f}")
print(f"🏷️ Final Prediction: {final_prediction}")

---

Predict other ion channel modulating proteins

---

🔧 Install Dependencies

pip install torch esm biopython huggingface_hub


### Loading the Model from Hugging Face

```python
import torch
import esm
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from transformers import AutoTokenizer, EsmForSequenceClassification

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


print("Downloading fine-tuned models & weights...")
repo_id = "anandr88/IonNTxPred"
subfolder = "saved_model_t33_other"
# Load the tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = EsmForSequenceClassification.from_pretrained(repo_id, subfolder=subfolder)
weights_path = hf_hub_download(repo_id=repo_id, filename="saved_model_t33_na/model.safetensors")

# Create a simple classifier model
class ProteinClassifier(torch.nn.Module):
    def __init__(self, esm_model):
        super().__init__()
        self.esm_model = esm_model
        # We'll dynamically determine the classifier layer size
        self.classifier = None
    
    def forward(self, tokens):
        with torch.no_grad():
            results = self.esm_model(tokens, repr_layers=[33], return_contacts=False)
        embeddings = results["representations"][33].mean(1)
        return self.classifier(embeddings)

# Initialize model
classifier = ProteinClassifier(model)

# Load the state dict and determine architecture
state_dict = load_file(weights_path, device=str(device))

# Find the classifier layer (look for a weight matrix)
for key, tensor in state_dict.items():
    if len(tensor.shape) == 2:  # This should be the weight matrix
        num_classes = tensor.shape[0]
        embedding_dim = tensor.shape[1]
        print(f"Found classifier layer: {key} (input_dim={embedding_dim}, output_dim={num_classes})")
        
        # Initialize the classifier layer
        classifier.classifier = torch.nn.Linear(embedding_dim, num_classes).to(device)
        
        # Create new state dict with proper names
        new_state_dict = {
            'classifier.weight': state_dict[key],
            'classifier.bias': state_dict[key.replace('weight', 'bias')]
        }
        classifier.load_state_dict(new_state_dict, strict=False)
        break

# Move to device and set to eval mode
classifier = classifier.to(device)
classifier.eval()

print(f"\nModel successfully loaded on {device} and ready for inference!")

🧪 Example Usage (Optional)

from transformers import AutoTokenizer, EsmForSequenceClassification
import torch

# Define the repository ID and subfolder
repo_id = "anandr88/IonNTxPred"
subfolder = "saved_model_t33_other"

# Load the tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = EsmForSequenceClassification.from_pretrained(repo_id, subfolder=subfolder)

# Move the model to the appropriate device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Function to make predictions
def make_predictions(model, inputs, device):
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.softmax(logits, dim=1)[:, 1].cpu().numpy()
    return probs

# Example protein sequence
protein_sequence = "MKASTLVVIFIVIFITISSFSIHDVQASGVEKREQKDCLKKLKLCKENKDCCSKSCKRRGTNIEKRCR"

# Tokenize the input sequence
inputs = tokenizer(protein_sequence, return_tensors="pt", truncation=True, padding=True)
inputs = {key: value.to(device) for key, value in inputs.items()}

# Make predictions
prediction = make_predictions(model, inputs, device)

# Apply threshold for final classification
threshold = 0.5
final_prediction = "Other channel modulating" if prediction[0] > threshold else "Not Other channel modulating"

print(f"📊 Prediction Probability: {prediction[0]:.4f}")
print(f"🏷️ Final Prediction: {final_prediction}")

---

---

📊 Applications

• Ion channel impairing proteins filtering in therapeutic design
• Toxicity scanning of synthetic peptides
• Dataset annotation for bioactivity studies
• Educational use in bioinformatics and deep learning for proteins

---

🌐 Related Links

• 🔬 Project Web Server: IonNTxpred Web Tool
• 🧾 Documentation & Source: GitHub – raghavagps/IonNTxPred

---

🧠 Citation

📖 Rathore et al.
A Large Language Model for Predicting Neurotoxic Peptides and Neurotoxins.
#Coming Soon#

---

👨‍🔬 Start using IonNTxPred today to enhance your protein/peptide screening pipeline with the power of transformer-based intelligence!