├── .gitignore ├── LICENSE └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/latest/usage/project/#working-with-version-control 110 | .pdm.toml 111 | .pdm-python 112 | .pdm-build/ 113 | 114 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 115 | __pypackages__/ 116 | 117 | # Celery stuff 118 | celerybeat-schedule 119 | celerybeat.pid 120 | 121 | # SageMath parsed files 122 | *.sage.py 123 | 124 | # Environments 125 | .env 126 | .venv 127 | env/ 128 | venv/ 129 | ENV/ 130 | env.bak/ 131 | venv.bak/ 132 | 133 | # Spyder project settings 134 | .spyderproject 135 | .spyproject 136 | 137 | # Rope project settings 138 | .ropeproject 139 | 140 | # mkdocs documentation 141 | /site 142 | 143 | # mypy 144 | .mypy_cache/ 145 | .dmypy.json 146 | dmypy.json 147 | 148 | # Pyre type checker 149 | .pyre/ 150 | 151 | # pytype static type analyzer 152 | .pytype/ 153 | 154 | # Cython debug symbols 155 | cython_debug/ 156 | 157 | # PyCharm 158 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 159 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 160 | # and can be added to the global gitignore or merged into this file. For a more nuclear 161 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 162 | #.idea/ 163 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Patrick Walters 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Machine Learning in Drug Discovery Resources 2025 2 | 3 | ### Books 4 | 5 | [Drug Design: From Structure and Mode-of-Action to Rational Design Concepts](https://www.amazon.com/Drug-Design-Structure-Mode-Action/dp/3662689979) 6 | 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. 7 | 8 | [Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) 9 | 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. 10 | 11 | [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. 12 | 13 | [Statistics in a Nutshell: A Desktop Quick Reference](https://www.amazon.com/Statistics-Nutshell-Desktop-Quick-Reference/dp/1449316824/) 14 | [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/) 15 | To effectively apply cheminformatics, one needs a solid grasp of statistics. This book provides a good overview with code examples. 16 | 17 | [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/) 18 | 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. 19 | 20 | 21 | ### Datasets 22 | 23 | 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 24 | consult this [blog post](https://practicalcheminformatics.blogspot.com/2023/08/we-need-better-benchmarks-for-machine.html). 25 | 26 | [OpenADMET](https://openadmet.org) seeks to proactively characterize the chemical space accessible to 27 | ADMET-associated proteins (“anti-targets”). By applying recent advances in experimental and computational techniques, a 28 | comprehensive open library of experimental and structural datasets will be generated. It's early days for OpenADMET, but 29 | knowing the folks involved, I'm highly optimistic. 30 | 31 | [AIRCHECK](https://aircheck.ai) is a platform that provides access to a large collection of high-quality datasets for drug discovery and 32 | development. The datasets are curated from various sources and are available in a standardized format. The current 33 | focus appears to be on DNA-encoded library (DEL) data. 34 | 35 | [Polaris](https://polarishub.io) aims to improve the state of benchmarking so ML can have a more significant impact on real-world drug discovery 36 | scenarios. To start, Polaris hopes to provide a single source of truth that aggregates and provides simple access to 37 | datasets & benchmarks. 38 | 39 | [PLINDER](https://plinder.sh) is an academic-industry collaboration to collect and organize protein-ligand interaction data. The effort is 40 | driven by VantAI, NVIDIA, the Computational Structural Biology group at the University of Basel & SIB Swiss Institute 41 | of Bioinformatics (co-organizers of CASP), and MIT. PLINDER aims to provide a gold standard dataset and evaluations 42 | to push the field of computational protein-ligand interactions prediction forward. 43 | 44 | ### Blogs 45 | 46 | [Eric J Ma's Website](https://ericmjl.github.io/) 47 | Eric's blog provides an excellent introduction to the application of cutting-edge informatics in drug discovery. 48 | 49 | [Oxford Protein Informatics Group (OPIG)](https://www.blopig.com/blog) 50 | This blog contains a lot of great [Bio|Chem]informatics content, chock-full of code. 51 | 52 | [Charlie’s Substack](https://harrisbio.substack.com/) 53 | Charlie Harris writes about applications of AI in drug discovery. Most recently, his posts have focused on efforts 54 | to reproduce AlphaFold3. 55 | 56 | [Mogan Thomas' Cheminformatics Blog](https://cheminformantics.blogspot.com/) 57 | This one is new, but it looks promising based on the first post. 58 | 59 | [Jon Swain's Blog](https://jonswain.github.io/) 60 | Jon Swain, a second-generation Cheinformatics blogger, has a great set of Jupyter notebooks demonstrating key concepts. 61 | 62 | [Practical Cheminformatics](https://practicalcheminformatics.blogspot.com/) 63 | This is a blog where I post once a month or so. These posts typically contain code demonstrating various aspects 64 | of cheminformatics; clustering, machine learning, data visualization, etc. I occasionally post 65 | opinions on things like AI and getting a job. 66 | 67 | [Is Life Worth Living](https://iwatobipen.wordpress.com/) 68 | A great blog from Iwatobipen (aka pen), whose posts are 69 | 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. 70 | 71 | [The RDKit Blog](http://rdkit.blogspot.com/) 72 | Greg Landrum is the primary contributor to and BDFL of the RDKit. In 73 | addition to the latest and greatest features in the RDKit, Greg's posts also touch on a number of key issues in 74 | Cheminformatics, such as dealing with unbalanced datasets and the impact of fingerprint folding on similarity searching. 75 | 76 | [Models to molecules](https://driesvr.github.io/) 77 | A new blog by Dries Van Rompaey that is off to a great start. 78 | 79 | ### Tutorials 80 | 81 | [Practical Cheminformatics Tutorials](https://github.com/PatWalters/practical_cheminformatics_tutorials) 82 | I put together this collection of Jupyter notebooks to demonstrate various aspects of cheminformatics and 83 | machine learning. The notebooks illustrate a range of topics from cheminformatics basics to more advanced 84 | machine learning. The tutorials all use open source software and can run on Google Colab without installing software 85 | locally. 86 | 87 | [TeachOpenCADD](https://github.com/volkamerlab/TeachOpenCADD) 88 | 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. 89 | 90 | [The RDKit Cookbook](https://www.rdkit.org/docs/Cookbook.html) 91 | A terrific resource that provides "recipes" for a number of common tasks. 92 | 93 | [Vina Colab Tutorials](https://autodock-vina.readthedocs.io/en/latest/colab_examples.html) 94 | A tutorial set shows how to run Autodock Vina and the associated protein and ligand setup utilities on Google Colab. 95 | 96 | [GNNs for Chemists](https://github.com/HFooladi/GNNs-For-Chemists) 97 | A great introduction to graph neural networks (GNNs) by Hosein Fooladi. 98 | 99 | [PDB-101 from the RCSB PDB](https://pdb101.rcsb.org/train/training-events) 100 | The Protein Databank (PDB) has a wide range of tutorials available. The Python scripting tutorials are very good. 101 | 102 | --------------------------------------------------------------------------------