# Introduction

## Overview

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.

{% hint style="warning" %}
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.
{% endhint %}

### Resources <a href="#resources" id="resources"></a>

All the course material and content can be found in the [Ersilia Introductory Workshop](https://github.com/ersilia-os/ersilia-intro-workshop) GitHub repository.

If new models are developed as part of the module 3 practice, those will be also added to the GitHub repository. The course surveys are developed using Mentimeter, but another software can be used for facilitation if preferred.

All content is released under a CC-BY-4 license.

## Funding

This workshop, delivered in September-OCtober 2023 has been possible thanks to the sponsorship of the Calestous Juma Fellowship (Bill and Melinda Gates Foundation) awarded to Prof. Ntie-Kang.


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