REDIAL - 2020

This repository contains ML models for predicting Anti-SARS-CoV-2 Activities (Live Virus Infectivity, Viral Entry, Viral Replication) and for performing Similarity Search

• Website: http://drugcentral.org/Redial

Table of Contents

• Requirements
• Installation and Initialization
  • Docker
    • Run Docker Images
    • Build your own images
  • Manual Start
• Docker API Content
• Contributors
• Funding
• Acknowledgements
• License

Requirements

At the moment, a standard machine with CPUs will work.

Installation and Initialization

Currently, we suggest two ways to run REDIAL-2020 locally: Docker and manually running from source. With Docker, you can either pull an image from DockerHub, or build one on your own.

Docker

To install Docker, just follow the docker documentation.

1. Run Docker Images

The latest Redial-2020 images are available on the Docker Hub.

• docker pull sirimullalab/redial-2020:latest
• docker run -p 5000:5000 sirimullalab/redial-2020:latest
• curl -F smiles='CCCC' localhost:5000/predict OR curl -d 'smiles=CCCC' localhost:5000/predict

2. Build your own images

1. git clone this repo
2. cd /path/to/this/repo
3. docker build --build-arg USER=$USER --build-arg UID=$UID --build-arg GID=$GID -t redial-2020 .
4. docker run -p 5000:5000 redial-2020
5. curl -F smiles='CCCC' localhost:5000/predict OR curl -d 'smiles=CCCC' localhost:5000/predict

NOTE: The API accepts form-request / post-request.

Manual Start

1. Install Miniconda, for your operating system, from https://conda.io/miniconda.html
2. git clone this repo
3. cd /path/to/this/repo
4. conda env create -f environment-redial-2020.yml
5. conda activate redial-2020 (or source activate redial-2020 for older versions of conda)
6. cd batch_screen
7. Example Commands :

python3 run_predictions.py --csvfile <PATH_TO_CSV_FILE> --results <PATH_TO_SAVE_RESULTS>

Note:

1. Input csv file should contain atleast one column having SMILES as column name.

Docker API Content

API URL: hostname:port/predict

• Found here - ./docker_api_content.yml

Contributors

Srijan Verma, Govinda KC, Mahmudulla Hassan, Giovanni Bocci, Suman Sirimulla, Tudor I. Oprea

Citation

Please cite: KC, G.B., Bocci, G., Verma, S. et al. A machine learning platform to estimate anti-SARS-CoV-2 activities. Nat Mach Intell 3, 527–535 (2021). https://doi.org/10.1038/s42256-021-00335-w

@Article{KC2021,
author={KC, Govinda B. and Bocci, Giovanni and Verma, Srijan and Hassan, Md Mahmudulla and Holmes, Jayme and Yang, Jeremy J. and Sirimulla, Suman and Oprea, Tudor I.},
title={A machine learning platform to estimate anti-SARS-CoV-2 activities},
journal={Nature Machine Intelligence},
year={2021},
month={Jun},
day={01},
volume={3},
number={6},
pages={527-535},
issn={2522-5839},
doi={10.1038/s42256-021-00335-w},
url={https://doi.org/10.1038/s42256-021-00335-w}
}

Acknowledgments

Suman Sirimulla acknowledge support from the National Science Foundation with NSF-PREM grant #DMR-1827745.

License

MIT License