🎉 Welcome to the Ersilia Model Hub 🌟

Table of Contents

1. Project Description
2. Quick start guide
3. Contribute
4. License and citation
5. About us

Project Description

The Ersilia Model Hub is a unified platform of pre-trained AI/ML models dedicated to 🦠 infectious and neglected disease research. Our mission is to offer an open-source, 🛠 low-code solution that provides seamless access to AI/ML models for 💊 drug discovery. Models housed in our hub come from two sources:

1. 📚 Published models from literature (with due third-party acknowledgement)
2. 🛠 Custom models developed by the Ersilia team or our valued contributors.

• Read more about the project in the Ersilia Book
• Browse available models in the Ersilia Model Hub

Quick Start Guide

Please check the package requirements in the Installation Guide. The next steps are a quickstart guide to installing Ersilia.

1. Create a conda environment and activate it

   conda create -n ersilia python=3.10
   conda activate ersilia
   

2. Clone this repository and install with pip

   git clone https://github.com/ersilia-os/ersilia.git
   cd ersilia
   pip install -e .
   

3. Once the Ersilia Model Hub is installed, you can use the CLI to run predictions. First, select a model from the Ersilia Model Hub and fetch it:

   ersilia fetch retrosynthetic-accessibility
   

4. Generate a few (5) example molecules, to be used as input. The example command will generate the adequate input for the model in use

   ersilia example retrosynthetic-accessibility -n 5 -f my_molecules.csv
   

5. Then, serve your model:

   ersilia serve retrosynthetic-accessibility
   

6. And run the model:

   ersilia run -i my_molecules.csv -o my_predictions.csv
   

7. Finally, close the service when you are done.

   ersilia close
   

8. If you no longer want to use the model, you can delete it.

   ersilia delete retrosynthetic-accessibility
   

Please see the Ersilia Book for more examples and detailed explanations.

Contribute

The Ersilia Model Hub is a Free, Open Source Software and we highly value new contributors. There are several ways in which you can contribute to the project:

• A good place to start is checking open issues.
• If you have identified a bug in the code, please open a new issue using the bug template.
• Share any feedback with the community using GitHub Discussions for the project
• Check our Contributing Guide for more details

The Ersilia Open Source Initiative adheres to the Contributor Covenant code of conduct.

Submit a New Model

If you want to incorporate a new model in the platform, open a new issue using the model request template or contact us using the following form.

After submitting your model request via an issue (suggested), a maintainer will review your request. If they /approve your request, a new model respository will be created for you to fork and use! There is a demo repository explaining the steps one-by-one.

License and Citation

This repository is open-sourced under the GPL-3 License. Please cite us if you use it!

Authorship

Please note that Ersilia distinguises between software contributors and software authors. The Ersilia Model Hub Authorship guidelines can be found in the Authorship file and current authors can be found in the Citation file. We acknowledge past authors of the software below:

• Carolina Caballero

Cited by

The Ersilia Model Hub is used in a number of scientific projects. Read more about how we are implementing it in:

• Turon, Hlozek et al, Nat Commun, 2023
• Van Heerden et al, ACS Omega, 2023
• Offensperger et al, Science, 2024
• Turon et al, ACS Med Chem Lett, 2024

About Us

The Ersilia Open Source Initiative is a Non Profit Organization with the mission is to equip labs, universities and clinics in LMIC with AI/ML tools for infectious disease research. Help us achieve our mission!

Funding

The Ersilia Model Hub is the flagship product of Ersilia. It has been funded thanks to a combination of funding sources. Full disclosure can be found in our website. Highlighted supporters include Splunk Pledge, the Mozilla Builders Accelerator and the AI2050 Program by Schmidt Futures.