You can deploy this model and start earning money today!

Neeraj0101/
AMP-Predict

Name: AMP-Predict
Rating: 2.5 (6 reviews)

safetensors

bert

custom_code

AIRI-Institute/gena-lm-bert-base-t2t

finetune

unknown

Views

No views yet

AMPPred — Antimicrobial Peptide Prediction Tool

AMPPred is a Python package with a BERT-based deep learning model for predicting antimicrobial peptides (AMPs) from nucleotide sequences. It supports raw nucleotide sequences as well as coding sequences extracted via gene prediction, enabling high-accuracy AMP classification.

Model Overview

Fine-tuned BERT architecture based on the GENA-LM framework for sequence classification
Trained on curated AMP datasets from APS (UNMC) and dbAMP (CUHK), with validated negative samples from NCBI
Achieves 99.05% accuracy on test data
Supports positional embeddings, sparse attention, and sequence length up to 256 tokens

Features

Predict whether nucleotide sequences encode antimicrobial peptides
Optional gene prediction using Pyrodigal to extract coding regions
Handles FASTA nucleotide inputs with batch processing
Outputs CSV reports with prediction confidence and optional FASTA of predicted AMPs
GPU acceleration supported

Usage Example

python

from amppred.utility.helper import load_model, get_predictions, read_fasta

model, tokenizer = load_model('Neeraj0101/AMP-Predict')
sequence_df = read_fasta('sequences.fasta')
predicted_df, _ = get_predictions(model, tokenizer, sequence_df)

Or via CLI:

bash

amp-build --model Neeraj0101/AMP-Predict
amp-run --input sequences.fasta --get_amp

Citation

If you use this model, please cite:

bibtex

@misc{amppred2025,
  title={AMPPred: Antimicrobial Peptide Prediction Tool},
  author={Singh, Neeraj Kumar},
  year={2025},
  url={https://github.com/neeraj1014/Antimicrobial-Peptide-Predictor}
}