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+---
+
+## Machine Learning in Drug Discovery Resources 2025
+
+### Books
+
+[Drug Design: From Structure and Mode-of-Action to Rational Design Concepts](https://www.amazon.com/Drug-Design-Structure-Mode-Action/dp/3662689979)
+As cheminformatics practitioners, we need to understand the drug design process.  This book, written by Prof. Gerhard Klebe, a pioneer in the field, provides an excellent overview of numerous drug design approaches.
+
+[Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X)
+Programming and data science are critical elements of cheminformatics.  This book, written by Wes McKinney, the author of the widely used Pandas library, provides a great starting point for learning Python and applying it in data science.
+
+[Data Science from Scratch: First Principles with Python](https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/1492041130/) This book provides another great introduction to data science. It provides an introduction to several critical topics, including Python, Statistics, Probability, Machine Learning, Clustering, and Databases.
+
+[Statistics in a Nutshell: A Desktop Quick Reference](https://www.amazon.com/Statistics-Nutshell-Desktop-Quick-Reference/dp/1449316824/)
+[Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python](https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-learning/dp/1801819319/)
+To effectively apply cheminformatics, one needs a solid grasp of statistics.  This book provides a good overview with code examples.
+
+[Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python](https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-learning/dp/1801819319/)
+Machine learning (ML) has become an integral component on cheminformatics.  This book provides a fantastic introduction to more traditional ML approaches and recent advances in deep learning.
+
+
+### Datasets
+
+You'll notice the conspicuous absence of two widely used datasets, [MoleculeNet](https://moleculenet.org/) and the [Therapeutic Data Commons (TDC)](https://tdcommons.ai/), from this list.  Both of these datasets are highly flawed and should not be used.  For more on the reasons why, please
+consult this [blog post](https://practicalcheminformatics.blogspot.com/2023/08/we-need-better-benchmarks-for-machine.html). 
+
+[OpenADMET](https://openadmet.org) seeks to proactively characterize the chemical space accessible to
+ADMET-associated proteins (“anti-targets”). By applying recent advances in experimental and computational techniques, a
+comprehensive open library of experimental and structural datasets will be generated. It's early days for OpenADMET, but
+knowing the folks involved, I'm highly optimistic. 
+
+[AIRCHECK](https://aircheck.ai) is a platform that provides access to a large collection of high-quality datasets for drug discovery and
+development. The datasets are curated from various sources and are available in a standardized format. The current
+focus appears to be on DNA-encoded library (DEL) data.
+
+[Polaris](https://polarishub.io) aims to improve the state of benchmarking so ML can have a more significant impact on real-world drug discovery
+scenarios. To start, Polaris hopes to provide a single source of truth that aggregates and provides simple access to
+datasets & benchmarks.
+
+[PLINDER](https://plinder.sh) is an academic-industry collaboration to collect and organize protein-ligand interaction data. The effort is
+driven by VantAI, NVIDIA, the Computational Structural Biology group at the University of Basel & SIB Swiss Institute
+of Bioinformatics (co-organizers of CASP), and MIT. PLINDER aims to provide a gold standard dataset and evaluations
+to push the field of computational protein-ligand interactions prediction forward.
+
+### Blogs
+
+[Eric J Ma's Website](https://ericmjl.github.io/)
+Eric's blog provides an excellent introduction to the application of cutting-edge informatics in drug discovery.
+
+[Oxford Protein Informatics Group (OPIG)](https://www.blopig.com/blog)
+This blog contains a lot of great [Bio|Chem]informatics content, chock-full of code. 
+
+[Charlie’s Substack](https://harrisbio.substack.com/)
+Charlie Harris writes about applications of AI in drug discovery. Most recently, his posts have focused on efforts
+to reproduce AlphaFold3.
+
+[Mogan Thomas' Cheminformatics Blog](https://cheminformantics.blogspot.com/)
+This one is new, but it looks promising based on the first post. 
+
+[Jon Swain's Blog](https://jonswain.github.io/)
+Jon Swain, a second-generation Cheinformatics blogger, has a great set of Jupyter notebooks demonstrating key concepts. 
+
+[Practical Cheminformatics](https://practicalcheminformatics.blogspot.com/)
+This is a blog where I post once a month or so. These posts typically contain code demonstrating various aspects
+of cheminformatics; clustering, machine learning, data visualization, etc. I occasionally post
+opinions on things like AI and getting a job.
+
+[Is Life Worth Living](https://iwatobipen.wordpress.com/)
+A great blog from Iwatobipen (aka pen), whose posts are
+chock-full of great code examples. Pen always seems to be up on the latest methods and posts interesting examples on various topics ranging from quantum chemistry to machine learning.
+
+[The RDKit Blog](http://rdkit.blogspot.com/)
+Greg Landrum is the primary contributor to and BDFL of the RDKit. In
+addition to the latest and greatest features in the RDKit, Greg's posts also touch on a number of key issues in
+Cheminformatics, such as dealing with unbalanced datasets and the impact of fingerprint folding on similarity searching.
+
+[Models to molecules](https://driesvr.github.io/)
+A new blog by Dries Van Rompaey that is off to a great start. 
+
+### Tutorials
+
+[Practical Cheminformatics Tutorials](https://github.com/PatWalters/practical_cheminformatics_tutorials)
+I put together this collection of Jupyter notebooks to demonstrate various aspects of cheminformatics and
+machine learning. The notebooks illustrate a range of topics from cheminformatics basics to more advanced
+machine learning. The tutorials all use open source software and can run on Google Colab without installing software
+locally.
+
+[TeachOpenCADD](https://github.com/volkamerlab/TeachOpenCADD)
+A great set of tutorials from Andrea Volkamer's group that use open-source software to teach Computer-Aided Drug Design concepts, including molecular similarity, applications of machine learning, and pharmacophore analysis.
+
+[The RDKit Cookbook](https://www.rdkit.org/docs/Cookbook.html)
+A terrific resource that provides "recipes" for a number of common tasks.
+
+[Vina Colab Tutorials](https://autodock-vina.readthedocs.io/en/latest/colab_examples.html)
+A tutorial set shows how to run Autodock Vina and the associated protein and ligand setup utilities on Google Colab.
+
+[GNNs for Chemists](https://github.com/HFooladi/GNNs-For-Chemists)
+A great introduction to graph neural networks (GNNs) by Hosein Fooladi.
+
+[PDB-101 from the RCSB PDB](https://pdb101.rcsb.org/train/training-events)
+The Protein Databank (PDB) has a wide range of tutorials available.  The Python scripting tutorials are very good.
+