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.
For readability, the chapter notebooks only contain runnable code blocks and section titles. They omit the rest of the material in the book, i.e., text paragraphs, figures (unless generated as part of one of the code blocks), equations, and pseudocode. If you want to be able to follow what's going on, I recommend reading the notebooks side-by-side with your copy of the book!
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 Summer 2025. We recommend interacting with notebooks through Colab.
Purchase of the book through Manning's Early Access Program (MEAP) guarantees access to current and future chapters. I appreciate your patience and support!
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!
- 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 Dive 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
- 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
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.
If you wish to cite the book, you may use the following:
@book{flynn2025mldd,
title={Machine Learning for Drug Discovery},
author={Flynn, N.},
isbn={9781633437661},
url={https://www.manning.com/books/machine-learning-for-drug-discovery},
year={2025},
publisher={Manning Publications}
}