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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Machine Learning in Drug Discovery Resources 2024
| Date | Stars |
|---|---|
| 2026-07-31 | 264 |
| 2026-08-06 | 264 |
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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
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