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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.
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.
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
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# Deep Learning for Proteins (DL4Proteins) Workshops  **Welcome to DL4Proteins!**  The goal of the DL4Proteins notebook series is to democratize deep learning for protein design and prediction, arriving at a transformative moment in science. With the 2024 Nobel Prize in Chemistry awarded to David Baker, Demis Hassabis, and John Jumper for breakthroughs in computational protein design and structural prediction, this resource provides an accessible, hands-on introduction to the very tools and methodologies that shaped this revolution. By blending foundational machine learning principles with state-of-the-art approaches such as AlphaFold, RFDiffusion, and ProteinMPNN, DL4Proteins equips researchers, educators, and students with the knowledge to contribute to the future of protein engineering. These open-source notebooks bridge the gap between cutting-edge research and classroom learning, fostering a new generation of innovators in synthetic biology and therapeutics. The associated preprint presents a detailed pedagogical framework for this notebook series, describing the motivation, learning outcomes, and deep-learning principles underlying each notebook. #### Preprint: [DL4Proteins Jupyter Notebooks Teach how to use Artificial Intelligence for Biomolecular Structure Prediction and Design](https://arxiv.org/abs/2511.02128) The Jupyter notebooks below provide an introduction to the fundamental machine learning concepts and models currently utilized in the protein design space. Notebooks can be run in Google Colaboratory. **For figures and questions to render correctly, please set colab notebooks to light mode. ### Table of contents ### [Chapter 1: Neural Networks with NumPy](https://colab.research.google.com/github/Graylab/DL4Proteins-notebooks/blob/main/notebooks/WS01_NeuralNetworksWithNumpy.ipynb) ### [Chapter 2: Neural Networks with PyTorch](https://colab.research.google.com/github/Graylab/DL4Proteins-notebooks/blob/main/notebooks/WS02_NeuralNetworksWithPyTorch.ipynb) ### [Chapter 3: Convolutional Neural Networks](https://colab.research.google.com/github/Graylab/DL4Proteins-notebooks/blob/main/notebooks/WS03_ConvolutionalNeuralNetworks.ipynb) ### [Chapter 4: Language Models for Shakespeare and Proteins](https://colab.research.google.com/github/Graylab/DL4Proteins-notebooks/blob/main/notebooks/WS04_LMsForShakespeareAndProteins.ipynb) ### [Chapter 5: Language model embeddings transfer learning for downstream task](https://colab.research.google.com/github/Graylab/DL4Proteins-notebooks/blob/main/notebooks/WS05_LanguageModelEmbeddingsTransferLearningForDownstreamTask.ipynb) ### [Chapter 6: Introduction to AlphaFold](https://colab.research.google.com/github/Graylab/DL4Proteins-notebooks/blob/main/notebooks/WS06_IntroductionToAF.ipynb) ### [Chapter 7: Graph Neural Networks for Proteins](https://colab.research.google.com/github/Graylab/DL4Proteins-notebooks/blob/main/notebooks/WS07_GNNsForProteins.ipynb) ### [Chapter 8: Denoising Diffusion Probabilistic Models](https://colab.research.google.com/github/Graylab/DL4Proteins-notebooks/blob/main/notebooks/WS08_DenoisingDiffusionProbabilisticModels.ipynb) ### [Chapter 9: Putting it All Together - From RFDiffusion to ProteinMPNN to Alphafold](https://colab.research.google.com/github/Graylab/DL4Proteins-notebooks/blob/main/notebooks/WS09_PuttingItAllTogether_DesigningProteins.ipynb) ### [Chapter 10: Introduction to RFDiffusion - All Atom](https://colab.research.google.com/github/Graylab/DL4Proteins-notebooks/blob/main/notebooks/WS10_RFDiffusion_AllAtom.ipynb) If you have any issues, please put into [Issues tab](https://github.com/Graylab/DL4Proteins-notebooks/issues). This is a living repository - we are actively incorporating feedback! Authors: Michael F. Chungyoun, Sreevarsha Puvada, Gabriel Au, Courtney Thomas,
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0fb456f7bed4b0bd, topic:deep-learning
matched fp:0fb456f7bed4b0bd, topic:language-model
matched fp:0fb456f7bed4b0bd, topic:diffusion-models