Advance drug discovery research with quantum or classical computing
Overview
This is an open-source AWS Solution that allows researchers and quantum computing advocates to design and run computational studies in the drug discovery field. With this AWS Solution, you can access quantum computers through Amazon Braket. The Amazon Braket Hybrid Job feature allows you to use classical computing and quantum computing resources to evaluate experiment values such as cost, time, and performance. The AWS Solution comes with built-in sample code for certain drug discovery problems, such as molecular docking, protein folding, RNA folding, and retrosynthetic planning, to help you get started with quantum computing in the field.
Benefits
Amazon Braket Hybrid Job provides flexibility for you to use either quantum computing or classical computing resources, or both. You can evaluate and compare experimental values by testing the same problem with these resources.
This AWS Solution includes code for certain drug discovery use cases. You can learn and customize the code step-by-step in Jupyter Notebook.
The batch-run results are stored in Amazon Simple Storage Service (Amazon S3). You can find the visualization and comparison of different experiment results in Notebook.
Technical details
You can automatically deploy this architecture using the implementation guide and the accompanying AWS CloudFormation template.
Step 1
This AWS Solution deploys a notebook instance to allow users to conduct notebook experiments. Notebooks come with use cases for different drug discovery problems such as molecule unfolding, RNA folding, and protein folding.
Step 2
When used for the first time, the system will mirror and upload the dependent packages required for the experiment to Amazon Elastic Container Registry (Amazon ECR).
Step 3
This program uses Amazon Braket Hybrid Job for experiments.
Step 4
The experiment results are stored in Amazon Simple Storage Service (Amazon S3).
Step 5
Run multiple Hybrid Jobs in one experiment, and initiate the events in Amazon EventBridge when the Hybrid Job is completed.
Step 6
EventBridge initiates events which are then sent to AWS Lambda for analysis and summary.
Step 7
Lambda sends an analysis report to Amazon Simple Notification Service (Amazon SNS). All subscribers who have subscribed to this topic will receive this report by email. This step is optional, and you can specify the email for subscription notifications when deploying the AWS Solution.
Step 8
Return to Amazon SageMaker Notebook to run the code and analyze and display the experiment results.
Step 1
This AWS Solution deploys a notebook instance to allow users to conduct notebook experiments. Notebooks come with use cases for different drug discovery problems such as molecule unfolding, RNA folding, and protein folding.
Step 2
When used for the first time, the system will mirror and upload the dependent packages required for the experiment to Amazon Elastic Container Registry (Amazon ECR).
Step 3
This program uses Amazon Braket Hybrid Job for experiments.
Step 4
The experiment results are stored in Amazon Simple Storage Service (Amazon S3).
Step 5
Run multiple Hybrid Jobs in one experiment, and initiate the events in Amazon EventBridge when the Hybrid Job is completed.
Step 6
EventBridge initiates events which are then sent to AWS Lambda for analysis and summary.
Step 7
Lambda sends an analysis report to Amazon Simple Notification Service (Amazon SNS). All subscribers who have subscribed to this topic will receive this report by email. This step is optional, and you can specify the email for subscription notifications when deploying the AWS Solution.
Step 8
Return to Amazon SageMaker Notebook to run the code and analyze and display the experiment results.
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