"Machine Learning for Drug Discovery" - Code and Data Repository [Work In Progress]

👋 Welcome to the Machine Learning for Drug Discovery Repository!!!

This repository contains code and data for the first edition of Machine Learning for Drug Discovery (Manning Publications). The companion material within this repository covers introductory topics at the intersection of machine learning, deep learning, and drug discovery applied to real world scenarios in each chapter. The code and notebooks are released under the Apache 2.0 license.

We just launched the book through Manning's Early Access Program (MEAP)! With MEAP, the first five chapters are immediately available along with access to a forum where we can directly discuss the projects and material. You'll also have first access to later chapters as they are progressively released, accompanying your learning pace. Next chapter coming soon, where we'll replicate the findings of a recent high impact paper!

Note that this project is a work in progress and notebooks will be released as they are drafted. We anticipate a full release of the book in Fall 2024. We recommend interacting with notebooks through Colab.

💻 Getting Started

Open the repository in Colab to walk through the notebooks without needing to install anything!

If you want to run and modify the code locally, install Anaconda (or Miniconda) and git if you don't already have access to them. Clone this repository by typing the following within a terminal (ignoring the first $ character):

$ git clone https://github.com/nrflynn2/ml-drug-discovery.git
$ cd ml-drug-discovery

Set up a conda environment with prerequisite installs:

$ conda create --name ml-drug-discovery python=3.10 pip
$ conda activate ml-drug-discovery
$ pip install -r requirements.txt

Finally, start Jupyter in the terminal via jupyter notebook or through your favorite IDE to embark on an exciting journey. Happy learning!

📚 Table of Contents

Part 1: Fundamentals of Cheminformatics & Machine Learning

• Chapter 1: The Drug Discovery Process
• Chapter 2: Ligand-based Screening: Filtering & Similarity Searching
• Chapter 3: Ligand-based Screening: Machine Learning
• Chapter 4: Solubility Deep Dives with Linear Models
• Chapter 5: Classification: Cytochrome P450 Inhibition
• Chapter 6: Case Study: Small Molecule Binding to an RNA Target
• Chapter 7: Unsupervised Learning: Repurposing Drugs, Curating Compounds, & Screening Fragments

Part 2: Deep Learning for Molecules & Structural Biology

• Chapter 8: Introduction to Deep Learning
• Chapter 9: Generative Models for Library Design
• Chapter 10: Molecules as a Language
• Chapter 11: Drug-Target Binding Affinity with Transformers
• Chapter 12: Graph Neural Networks for Molecules
• Chapter 13: GNN Applications in Drug Discovery
• Chapter 14: Diffusion Models
• Chapter 15: Closing Remarks & Next Steps

👥 Contribution & Support

Feel free to contribute, raise issues, or propose enhancements to make this repository a comprehensive resource for everyone venturing into machine learning, drug discovery, and related applications.