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DL4Proteins Notebook Series Bridging Machine Learning and Protein Engineering: A Practical Guide to Deep Learning Tools for Protein Design
- DL4Proteins Notebook Series provides practical and hands-on resources integrating foundational machine learning concepts with advanced protein engineering methods for predicting and designing protein structures lines.
- The series offers accessible learning tools, ranging from neural networks to graph models, that enable researchers, educators, and students to apply deep learning techniques to protein design tasks lines.
- The notebooks include introductions to tools like AlphaFold, RFDiffusion, and ProteinMPNN aimed at fostering innovation in synthetic biology and therapeutics lines.
- Notebook 1 and Notebook 2 introduce the foundational concepts of neural networks using NumPy and PyTorch, respectively lines.
- Notebook 3 explains the foundational concepts of CNNs and demonstrates their application in handling image like data lines.
- Notebook 4 explores the use of LMs in understanding sequences such as text and proteins lines.
- Notebook 5 delves into the application of language model embeddings in solving real-world problems by repurposing embeddings generated from pre-trained language models lines.
- Notebook 6 introduces the use of GNNs in protein research, emphasizing their ability to model the complex relationships between amino acids in protein structures lines.
- Notebook 7 explores the application of diffusion models in protein structure prediction and design lines.
- Notebook 8 combines advanced tools like RFdiffusion, ProteinMPNN, and AlphaFold to guide users through the complete protein design process lines.
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