UniDL4BioPep

Universal Deep Learning Architecture for Bioactive Peptide Prediction

🔬 Implementation of "UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity" (Briefings in Bioinformatics, 2023)

📧 For large dataset processing, contact: zhenjiao.du@ucd.ie or yonghui@ksu.edu

Quick Prediction

1

Select Model

Choose prediction model

2

Input Sequence

Enter peptide sequence

3

Run Analysis

Get instant results

Choose Prediction Model:

Peptide Sequences:

Processing your sequences...

Batch Prediction

1

Upload File

Excel, FASTA, or TXT

2

Select Models

Choose multiple models

3

Download Results

Get comprehensive report

Upload File:

Click to upload or drag & drop

Supports: .xls, .xlsx, .txt, .fasta

Select Prediction Models:

🩺 Antihypertensive

💊 DPPIV Inhibitory

😖 Bitter Taste

😋 Umami Taste

🦠 Antimicrobial

🦟 Antimalarial (Alt)

🦟 Antimalarial (Main)

📡 Quorum Sensing

🎯 Anticancer (Alt)

🎯 Anticancer (Main)

🔬 Anti-MRSA

🧬 Tumor T-cell

🧠 Blood-Brain Barrier

🦠 Anti-parasitic

⚡ Neuropeptide

🛡️ Antibacterial

🍄 Antifungal

🛡️ Antiviral

☠️ Toxicity

🌿 Antioxidant

⚠️ Allergen

📥 Cell Penetrating

The prediction results will be automatically downloaded as a excel file, please be patient.