> For the complete documentation index, see [llms.txt](https://ersilia.gitbook.io/ersilia-workshops/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ersilia.gitbook.io/ersilia-workshops/ub-cedd-ml-for-drug-discovery/introduction.md).

# Introduction

This page describes the training materials developed by Ersilia and participants of the program on AI for Drug Discovery at the University of Buea Center for Drug Discovery.

## Overview

This course is part of a series delivered over five years (2022–2026) as part of the ongoing collaboration between Ersilia and the UB-CeDD, with each edition building on the previous one.

The goal of this course is to provide a first insight into the use of AI/ML tools for drug discovery, with a strong focus on the prediction of bioactivities against pathogens. Domain-relevant examples, including natural product databases are included in the course.

At course completion, students will be able to:

* Read and understand publications in the domain of computational biology
* Know where to look for public datasets to build AI/ML models
* Leverage open source tools like GitHub
* Expand on basic concepts of Python programming and Jupyter Notebooks
* Distinguish between several cloud and local-based computing systems
* Use already existing AI/ML models and apply them to their research

The course is geared towards graduate students and postdoctoral researchers with a background in biology, biomedicine or chemistry who want to focus their work in the exciting field of computational biology and data science! Preferably course participants should have active ongoing research projects.

This course is not aimed at computer scientists savvy in programming and data management. We provide foundations to understand how to apply AI/ML to research projects, not a deep-dive into AI/ML development.

## Course modules

The content in Course Modules has been delivered over five years, across four research visits of the Ersilia Team to the UB-CeDD, with each edition advancing from a first introduction to the full application of AI/ML tools to the students' own research.

## Facilitators <a href="#facilitators" id="facilitators"></a>

This course was facilitated by the [Ersilia Open Source Initiative](https://ersilia.io/). Feel free to reach out to the facilitators if you have any questions or concerns!

* Gemma Turon, PhD (<gemma@ersilia.io>). Co-Founder and Executive Director.
* Miquel Duran-Frigola, PhD (<miquel@ersilia.io>). Co-Founder and Science & Technology Director

The program was coordinated with UB-CeDD with the support of Ersilia's Programs & Parthership Manager Inés Vicente Navarro (<ines@ersilia.io>).

## Funding

This five-year series of workshops has been possible thanks to the sponsorship of the Calestous Juma Fellowship (Bill and Melinda Gates Foundation) awarded to Prof. Ntie-Kang for the NiDNA project.


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