Running AlphaFold on VertexAI | Google Cloud Blog

At Google Cloud, we believe the products we bring to market should be strongly informed by our research efforts across Alphabet. For example, Vertex AI was ideated, incubated and developed based on the pioneering research from Google’s research entities. Features like Vertex AI Forecast, Explainable AI, Vertex AI Neural Architecture Search (NAS) and Vertex AI Matching Engine were born out of discoveries by Google’s researchers, internally tested and deployed, and shared with data scientists across the globe as an enterprise-ready solution, each within a matter of a few short years. 

Today, we’re proud to announce another deep integration between Google Cloud and Alphabet’s AI research organizations: the ability in Vertex AI to run DeepMind’s groundbreaking protein structure prediction system, AlphaFold

We expect this capability to be a boon for data scientists and organizations of all types in the bio-pharma space, from those developing treatments for diseases to those creating new synthetic biomaterials. We’re thrilled to see Alphabet AI research continue to shape products and contribute to platforms on which Google Cloud customers can build. 

This guide provides a way to easily predict the structure of a protein (or multiple proteins) using a simplified version of AlphaFold running in a Vertex AI. For most targets, this method obtains predictions that are near-identical in accuracy compared to the full version. To learn more about how to correctly interpret these predictions, take a look at the “Using the AlphaFold predictions” section of this blog post below. 

Please refer to the Supplementary Information for a detailed description of the method.

Solution Overview

Vertex AI lets you develop the entire data science/machine learning workflow in a single development environment, helping you deploy models faster, with fewer lines of code and fewer distractions.

For running AlphaFold, we choose Vertex AI Workbench user-managed notebooks, which uses Jupyter notebooks and offers both various preinstalled suites of deep learning packages and full control over the environment. We also use Google Cloud Storage and Google Cloud Artifact Registry, as shown in the architecture diagram below.

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