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Machine learning has been revolutionizing all fields and has helped make waves in the research and development of drugs, identifying drug properties, understanding patterns, and deriving intelligent results. Drug development and its lifecycle are very tedious and time-consuming. With the right application of machine learning algorithms, biotech and life science companies can fast-track drug development and pace themselves to newer discoveries. With the advent of large language models and neural networks, there are specific use cases, where machine learning proves highly effective. Even while there is much research going on and in progress, the potential impact of applying machine learning is huge to the needs of the pharmaceutical and life sciences sector.


  • Understand how a machine learning model works
  • Understand ongoing research in the field of pre-clinical research and machine learning
  • Discuss use cases where machine learning algorithms will help
  • Advantages of machine learning in pre-clinical research
  • Companies working on fast-tracking pre-clinical research
  • Key takeaways 

  • Introduction to machine learning
  • How does an ML model work?
  • Applications of ML in pre-clinical research
  • Deep dive into ML use cases
  • Key takeaways and learnings
  • Avoiding data-specific pitfalls

  • Machine learning enthusiasts
  • Business Analysts
  • Business consultants
  • Life science leaders
  • Regulatory compliance experts

  • Over 17 years of experience in life sciences
  • Previous experience working with Pwc,KPMG and Deloitte,Oracle,MetricStream,Infosys Consulting, HTC and Siemens Research
  • Active member of ISACA
  • Professional associations with ISPE,OCEG,GARP,Carnegie Mellon Swartz Center for entrepreneurship
  • Master in Software Engineering, Carnegie Mellon University, Pittsburgh,US
  • Bachelors in Computer Science, University of Madras,India
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