Diff-AMP: An Integrated Tool for Antimicrobial Peptide Generation and Design Based on Diffusion and Reinforcement Learning Strategies

Introduction

We have developed a unified AMP (Antimicrobial Peptide) design and optimization framework called Diff-AMP, which integrates various deep learning techniques to efficiently accomplish the tasks of AMP generation, evaluation, and optimization. As depicted in the following diagram, this framework encompasses the entire process of automatic molecule design and optimization, including AMP generation, evaluation, property prediction, and multi-objective iterative optimization. The framework comprises four modules, each incorporating advanced deep learning technologies. In Module A, we, for the first time, combine diffusion strategies and attention mechanisms (Module B) within a GAN network, proposing a novel AMP generation model to create AMPs that meet specific properties. Module C introduces large-parameter pre-trained models to acquire general knowledge about nodes and efficiently infer generated potential AMPs. Module D introduces a CNN model to perform multi-property prediction for AMPs. Module E marks the introduction of reinforcement learning strategies for the iterative optimization of generated AMPs.

Environment

First, clone and navigate to the repository.

git clone https://github.com/wrab12/diff-amp
cd diff-amp

This process can take several minutes, depending on network speed.

Create and activate a virtual environment using python 3.9 with virtualenv or conda,

# virtualenv (python 3.9)
virtualenv env
source env/bin/activate

# conda
conda create -n diff-amp python=3.9
conda activate diff-amp

Install dependencies and the local library with pip.

pip install -r requirements.txt

This process usually takes around 5 minutes.

Datasets

• Find datasets for the generation model(Diff-RLGen), and the recognition model in the data directory.
• Download datasets for multi-property prediction from this Google Drive link.

Usage

generation model

• You can directly use our generation model through Hugging Face: Diff-AMP Antimicrobial Peptide Generation
• Train the generation model: Run gan_diff.py.
• Generate antimicrobial peptides: Use gan_generate.py. Get weight files from this Google Drive link and place them in the weight directory to use without retraining.

classification model

• You can directly use our recognition model through Hugging Face: Diff-AMP AMP Sequence Detector
• Train the classification model: Run AMP_Classification.py.
• If you want to directly identify your own peptides as antimicrobial, you can run AMP_Classification_Prediction.py. In this case, prepare a file named seq.txt with one sequence per line. You can modify the input format if needed. To use pre-trained weights, download them from this Google Drive link and place them in the weight directory.

Multi-Attribute Prediction Model

• You can directly use our multi-attribute prediction model through Hugging Face: Diff-AMP AMP Prediction Model
• Perform property prediction on generated antimicrobial peptides: Run predict.py. Get the necessary weight files from this Google Drive link and place them in the 'models' directory.

run

• Experience all steps of generation, recognition, and optimization.

run.sh