{"id":26187576,"url":"https://github.com/gmum/mldd23","last_synced_at":"2026-02-07T03:02:01.069Z","repository":{"id":158074292,"uuid":"594883298","full_name":"gmum/mldd23","owner":"gmum","description":"The repository for the course \"Machine Learning in Drug Design\" taught at the Jagiellonian University in 2023. 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Python and machine learning basics (revision)\n    - Textual representations of molecules: SMILES\n    - Vector representations of molecules: descriptors and fingerprints\n    - Introduction to RDKit\n    - Classical ML models: Linear Regression, Random Forest, Support Vector Machines\n2. Exploration of publicly available small molecule datasets\n    - ChEMBL database of bioactive molecules\n    - ZINC database of purchasable molecules\n    - PubChem database of chemical information about small molecules\n    - Exploratory Data Analysis (EDA)\n    - Quantitative Structure-Activity Relationship (QSAR) and Virtual Screening (VS)\n3. Graph neural networks\n    - Neural networks architectures and training\n    - Molecular graphs, atomic featurization\n    - Message passing neural networks\n    - Graph convolutional neural networks\n    - Explainability: Grad-CAM\n4. Molecular docking\n    - Molecular data formats: SMI, SDF, MOL2, PDB\n    - Force fields\n    - Protein folding\n    - Molecular docking with AutoDock Vina, Smina, QuickVina\n    - Interaction fingerprints\n    - Pharmacophores\n5. Deep generative models\n    - Autoencoders\n    - Recurrent neural networks\n    - SMILES generators: ReLeaSE\n    - Graph-based generative models: JT-VAE\n    - Reinforcement learning and Bayesian optimization for molecular property optimization\n6. Protein deep learning\n    - Simplified protein graph representations\n    - Voxel grid representation\n    - Mesh representation for encoding protein surface\n    - 3D convolutional neural networks for encoding proteins\n7. Uncertainty prediction\n    - Aleatoric and epistemic uncertainty\n    - Conformal prediction\n\n## Lectures\n\nThe lecture slides and notes are in the `lectures` directory.\nThe machine learning lecture is deployed online at:\n\n[gmum.github.io/mldd23/lectures/machine-learning.html](https://gmum.github.io/mldd23/lectures/machine-learning.html#/)\n\n## About us\n\n[GMUM](https://gmum.net/) (Machine Learning Research Group) is a group at the Jagiellonian University working on various aspects of machine learning, and in particular deep learning - in both fundamental and applied settings. The group is led by prof. Jacek Tabor.\n\nSome of the research directions our group pursues include:\n- generative models: efficient training and sampling; inpainting; super-resolution,\n- theoretical understanding of deep learning and optimization,\n- natural language processing,\n- drug design and cheminformatics,\n- unsupervised learning and clustering,\n- computer vision and medical image analysis.\n\nWe are hosting machine learning seminars that are open to the public. You can check the schedule on [our website](https://gmum.net/seminars.html) and join online (links posted on [our Facebook](http://facebook.com/gmum.net)).\nYou can also add seminar info to your [Google calendar](https://calendar.google.com/calendar/u/0?cid=ZDJjcTFudnU0Y2UxNXNnODltdDc4Y3BtcTBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ).\n\n## Environment Setup\n\nPython will be used throughout the course. The environment setup steps are shown below:\n\n1. Install [miniconda](https://docs.conda.io/en/latest/miniconda.html) following the instructions for your operating system.\n2. Download this repository: `git clone https://github.com/gmum/mldd23.git`.\n   - You need to have [Git](https://git-scm.com/) installed.\n3. Install environment from the YAML file: `conda env create -f environment.yml` (or `conda env create -f environment-gpu.yml` for the GPU version).\n\nIn the `environments` directory, you can find environment files with the exact versions of packages for each operating system (including a GPU environment for Windows).\n\n_Important! If you would like to use your GPU to train neural networks, update the line `pytorch-cuda={cuda version}` in the `environment-gpu.yml` file. The current CUDA version is 11.7, but you should check your graphics card compatibility first._\n\n## Literature\n\n???","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgmum%2Fmldd23","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgmum%2Fmldd23","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgmum%2Fmldd23/lists"}