Digital Chemistry: AI in Drug Discovery - Workshop

This repository contains the code and instructions for setting up for the AI in Drug Discovery workshop sessions.

Contents

During the workshop, we will be using a framework for machine learning in drug discovery called DeepChem. DeepChem is a Python library and toolchain that combines multiple machine learning algorithms, frameworks and datasets and provides a unified interface for training and evaluating models in drug discovery.

For each workshop session, we will be practically using DeepChem through a set of tutorial Jupyter Notebooks from the DeepChem library.

Workshop 1: Introduction to DeepChem (07/03/2024)

This workshop will introduce you to the basics of DeepChem and will allow you to familiarize yourself with the library.

1. Basic tools of the deep life sciences

   

2. Working with datasets

   

3. An introduction to Moleculenet

   

4. Molecular fingerprints

   

Workshops 2 and 3: Ligand and structure based predictive modelling (13/03/2024 and 20/03/2024)

This workshop covers lectures 3-5 of the AI in Drug Discovery lecture course.

1. Creating a High Fidelity Dataset from Experimental Data

   

2. Creating Models with TensorFlow and PyTorch

   

3. Going Deeper On Molecular Featurizations

   

4. Working With Splitters

   

5. Introduction to Graph Convolutions

   

Workshop 4: Modeling protein-ligand interactions (22/03/2024)

Please note these two notebooks need to be run using Colab.

1. Modeling Protein-Ligand Interactions

   

2. Modeling Protein-Ligand Interactions with Atomic Convolutions