Impact of machine learning in pre-clinical research and regulatory compliance
Machine learning is changing drug research and development, helping teams identify drug properties, find patterns and draw useful conclusions from data. This 60-minute webinar on machine learning in pre-clinical research explains how it can help biotech and life science companies speed up a long, tedious development process.
You'll start with how an ML model works, then look at applications in pre-clinical research and specific use cases where ML, including large language models and neural networks, is especially effective.
The session also reviews ongoing research, companies working to fast-track pre-clinical work, the advantages ML brings, and data-related pitfalls to avoid.
- Explain how a machine learning model works
- Describe ongoing research in machine learning for pre-clinical work
- Identify use cases where machine learning algorithms help
- Explain the advantages of machine learning in pre-clinical research
- Recognize companies working to fast-track pre-clinical research
- Avoid common data-specific pitfalls
- Introduction to machine learning
- How an ML model works
- Applications of ML in pre-clinical research
- A closer look at ML use cases
- Avoiding data-specific pitfalls
- Key takeaways
Drug development takes a long time, and machine learning offers a way to move faster and reach new discoveries. Research is still in progress, but the potential impact for pharma and life sciences is large, so teams need to understand what it can and can't do.
This session suits life science leaders, regulatory compliance experts, business analysts and consultants, and machine learning enthusiasts.
- 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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