ANTICP3: Anticancer Protein Prediction

This model is a fine-tuned version of facebook/esm2-t33-650M-UR50D designed for binary classification of anticancer proteins (ACPs) from their primary sequence.

Developed by: G. P. S. Raghava Lab, IIIT-Delhi

Model hosted by: Dr. GPS Raghava's Group

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Model Details

Feature	Description
Base Model	facebook/esm2_t33_650M_UR50D
Fine-tuned On	Anticancer Protein Dataset
Model Type	Binary Classification
Labels	0: Non-Anticancer 1: Anticancer
Framework	Transformers + PyTorch
Format	safetensors

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Usage

Use this model with the Hugging Face transformers library:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load tokenizer and fine-tuned model
tokenizer = AutoTokenizer.from_pretrained("raghavagps-group/anticp3")
model = AutoModelForSequenceClassification.from_pretrained("raghavagps-group/anticp3")

# Example protein sequence
sequence = "MANCVVGYIGERCQYRDLKWWELRGGGGSGGGGSAPAFSVSPASGLSDGQSVSVSVSGAAAGETYYIAQCAPVGGQDACNPATATSFTTDASGAASFSFVVRKSYTGSTPEGTPVGSVDCATAACNLGAGNSGLDLGHVALTFGGGGGSGGGGSDHYNCVSSGGQCLYSACPIFTKIQGTCYRGKAKCCKLEHHHHHH"

# Tokenize and run inference
inputs = tokenizer(sequence, return_tensors="pt", truncation=True)

with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.nn.functional.softmax(logits, dim=-1)
    prediction = torch.argmax(probs, dim=1).item()

labels = {0: "Non-Anticancer", 1: "Anticancer"}
print("Prediction:", labels[prediction])