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AI-powered bioinformatics solution for drug response prediction.


Drug response prediction platform / 12-month engagement / 1-person team

Drug response prediction platform development case study.png

About the project

Our client is a biotech company that focuses on developing DNA-based infectious disease solutions for drug response prediction to improve the way antimicrobial-resistant infections are diagnosed and prevented or treated. The platform combines genomic analysis with artificial intelligence to provide accurate and rapid predictions of drug susceptibility for bacterial infections helping healthcare providers, clinicians, and microbiologists to make more informed antimicrobial treatment decisions.

Product features

Genomic sequencing

The platform uses whole-genome sequencing to analyze the DNA of bacterial and viral pathogens allowing healthcare providers to identify the specific strain of the pathogen and determine its drug susceptibility profile.

AI & ML algorithms

The solution uses machine learning algorithms that have been trained on large datasets of genomic data to analyze the genomic data and predict the susceptibility of bacteria to different drugs.

Predictive modeling

Based on the genetic markers identified in the genomic data, predictive modeling helps identify which antimicrobial drugs are most likely to be effective against a particular strain of bacteria.

Epidemiological surveillance

This feature allows healthcare professionals to track the prevalence and spread of antibiotic-resistant bacteria which can help to identify outbreaks and prevent the spread of resistant strains. The platform can also be used to monitor changes in resistance patterns over time, which can inform public health strategies and interventions.

Data management and reporting

The platform provides a detailed report that includes information on the genetic markers associated with drug resistance, as well as recommendations for the most effective treatment options. AWS Amplify and AWS AppSync were used to automate data processing pipelines with a data interpretation system.

AWS infrastructure

To enable labs globally the client developed a cloud-based data interpretation system supported by an AMR knowledge base. AWS Batch helps scale and optimize training algorithms, while a CLIA-compliant workflow and cloud-based system automate pathogen identification, AMR characterization, and genotyping-assisted outbreak analysis.


Software Engineering


  • Frontend: Vue, Amplify, TypeScript, Vuetify

  • Backend: NodeJS, Python, AWS Lambda 

  • Data: DynamoDB, AWS RDS, Alembic, Cognito, Sqlalchemy, GraphQL

  • Cloud: AWS, Docker

Product Team

1 engineer

Drug response prediction platform_AMR annotation and profiling dashboard view

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Let's talk 

Interested to learn more on how Radency can bring value to your business? Drop us a line! 

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