Add AlphaFold 3 quickstart notebook (#4572)

* Add AlphaFold 3 quickstart notebook

* Update CODEOWNERS

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Co-authored-by: Amit Rai <raiamit@google.com>

notebooks/community/CODEOWNERS

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 /vertex_endpoints/optimized_tensorflow_runtime @vlasenkoalexey
 /notebooks/community/alphagenome/cloudai_alphagenome_vai_quickstart.ipynb @dpanigra
 /notebooks/community/alphagenome/cloudai_alphagenome_finetune.ipynb @dpanigra
+/notebooks/community/alphafold3/cloudai_alphafold3_vai_quickstart.ipynb @raiamitgit
 /notebooks/community/weathernext/weathernext_2_early_access_program.ipynb @dpanigra
 /notebooks/community/weathernext/weathernext_2_ic_early_access_program.ipynb @dpanigra
 /notebooks/community/weathernext/weathernext_2_dws.ipynb @dpanigra

notebooks/community/alphafold3/README.md

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+# AlphaFold 3
+[**Overview**](#overview) | [**Use cases**](#use-cases) | [**Documentation**](#documentation) | [**Prerequisites**](#prerequisites) | [**Quick start**](#quick-start)
+
+## Overview
+AlphaFold 3 is a revolutionary model developed by Google DeepMind and Isomorphic Labs that predicts the 3D structures and interactions of proteins, DNA, RNA, ligands, and chemical modifications.
+
+By modeling these molecules and their interactions together in a unified diffusion-based architecture, AlphaFold 3 provides a comprehensive view of cellular machinery, enabling researchers to understand biological processes at atomic resolution.
+
+AlphaFold 3 is available for commercial use on [Gemini Enterprise Agent Platform](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/open-models/alphafold-3).
+
+## Use cases
+* **Protein-Ligand Interaction Prediction**: Model the binding of small molecule ligands to proteins, enabling drug discovery and development.
+* **Nucleic Acid Interaction Prediction**: Predict the complex structures of proteins interacting with DNA and RNA sequences.
+* **Chemical Modifications**: Predict structures containing modified residues, ions, and covalent linkages.
+* **Antibody-Antigen Modeling**: Map the 3D structures of antibody-antigen complexes to support therapeutic antibody design.
+
+## Documentation
+The examples provided here demonstrate how to deploy and use AlphaFold 3 on Gemini Enterprise Agent Platform.
+
+### Links
+* Read the [Nature journal paper](https://doi.org/10.1038/s41586-024-07487-w)
+* Read the [Google DeepMind blog post](https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/)
+* Explore the [AlphaFold Server](https://alphafoldserver.com/welcome)
+* View the open-source code and non-commercial weights on [GitHub](https://github.com/google-deepmind/alphafold3)
+
+## Prerequisites
+To deploy and use AlphaFold 3 on Vertex AI:
+1. **Request Access**: Submit the [AlphaFold 3 Request Form](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/alphafold3-request) and work with your Google Cloud account team for commercial subscription allowlisting.
+2. **Hardware Quota**: Deployments require an `a3-highgpu-1g` machine type (1x NVIDIA H100 80GB GPU) with 750 GB Local SSD provisioned for database caching.
+3. **Endpoint Configuration**: Deploy the model to a Dedicated Endpoint and configure the inference timeout to 3,600 seconds.
+
+## Quick start
+| Notebook | Description | Links |
+| :--- | :--- | :--- |
+| [AlphaFold 3 Quickstart](cloudai_alphafold3_vai_quickstart.ipynb) | End-to-end protein-ligand docking prediction (KRAS G12C covalent complex with Sotorasib), output handling, and 3D visualization. | <a href="https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/alphafold3/cloudai_alphafold3_vai_quickstart.ipynb"><img src="https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg" alt="Open in Colab" height="20"></a> |