DeepAVC is a deep learning framework for highly accurate broad-spectrum antiviral compound prediction.
Lethal viruses pose a significant threat to human life, with each pandemic causing millions of fatalities globally. Small-molecule antiviral drugs provide an efficient and convenient approach to antiviral therapy by either inhibiting viral activity or activating the host immune system. However, conventional antiviral drug discovery is often labor-intensive and time-consuming due to the vast chemical space. Although some existing computational models mitigate this problem, there remains a lack of rapid and accurate method specifically designed for antiviral drug discovery. Here, we propose DeepAVC, a universal framework based on pre-trained large language models, for highly accurate broad-spectrum antiviral compound discovery, including DeepPAVC for phenotype-based prediction and DeepTAVC for target-based prediction. We demonstrate the power of DeepAVC in antiviral compound discovery through a series of in silico and in vitro experiments, identifying MNS and NVP-BVU972 as two novel potential antiviral compounds with promising broad-spectrum antiviral activities.
DeepPAVC model only takes compound information as input, utilizes a pre-trained molecular encoder to extract compound features, and outputs the antiviral activity score of the input compound
DeepTAVC model requires input from two modalities: compounds and proteins. It employs two distinct pre-trained encoders to extract features from each modality separately, then captures intra- and inter- modality interaction patterns through self-attention and cross-attention mechanisms, and outputs the interaction score between the compound and the protein
Highly accurate prediction of broad-spectrum antiviral compounds with DeepAVC
- python=3.7.13
- pytorch=1.10.0
- cudatoolkit=11.3.1
- scikit-learn=1.0.2
- pandas=1.3.5
- numpy=1.21.5
- fair-esm=2.0.0
- rdkit=2021.09.2
Step1: clone the repo
mkdir ./DeepPAVC
cd DeepPAVC
git clone https://github.com/KangBoming/DeepAVC.git
Step2: create and activate the environment
cd DeepPAVC
conda env create -f environment.yml
conda activate DeepPAVC
Step3: model training
cd DeepPAVC
Please follow DeepPAVC_train.ipynb
Please follow DeepTAVC_train.ipynb
Step4: model infernece
cd DeepPAVC
Please follow DeepPAVC_inference.ipynb
Please follow DeepTAVC_inference.ipynb
This project is licensed under the MIT License - see the LICENSE.txt file for details
Please feel free to contact us for any further queations
Boming Kang kangbm@bjmu.edu.cn
Qinghua Cui cuiqinghua@bjmu.edu.cn