CASE STUDY

AI Assisted Drug discovery

Client is a leading pharmaceutical company that supports millennials’ health with its wide range of affordable medicinal products. They wanted to accelerate research by using ML models for better drug discovery and reduce costs. 

Key Challenges

  • Client has a platform where researchers can access data regarding drug candidates, gene expressions, protein-protein interactions, and clinical records 
  • They wanted to integrate rapid analytics and machine learning capabilities into this application to enhance the drug discovery process
  • They wanted to improve the probability of FDA approval to make drugs more affordable 

Technologies used

Solution

  • We analyzed the existing architecture of the client’s application and re-designed it to support cognitive functionalities 
  • Data sets available involved millions of compounds, we cleansed and normalized data from internal and external sources (public libraries) for predicting therapeutic potential 
  • Designed and developed ML models for optimizing formulation conditions for proteins based on available in-house data and externally available stability data
  • Implemented appropriate data governance and management principles for reusability of existing models
  • Integrated with third-party modeling and visualization tools for enhanced/better drug discoveries using knowledge graphs 

Benefits

  • Helped them reduce research and development costs by 25%, while avoiding costly errors
  • Improved regulatory compliance, and increased collaboration between teams and departments
  • Enabled faster data-driven decisions to gain insight into business

Let’s get started

Find out how Sparity uses AI for drug discovery

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