Beyond the Funnel: Lessons from Integrating Generative AI and Active Drug Discovery

Overview

Explore how generative AI and active drug discovery integrate in Richard Bonneau's talk on moving beyond traditional approaches in pharmaceutical research.

Syllabus

• Introduction to Generative AI in Pharmaceutical Research
Overview of generative AI techniques
Historical context and evolution in drug discovery
• Active Drug Discovery: Concepts and Processes
Traditional vs. active drug discovery methods
Key challenges in traditional approaches
• Integration of Generative AI and Active Drug Discovery
Synergistic effects of combining techniques
Case studies of successful integrations
• Generative AI Models in Drug Discovery
Deep learning and neural networks
Generative adversarial networks (GANs)
Variational autoencoders (VAEs)
• Data Requirements and Management
Types of data needed for generative models
Data preprocessing and cleaning techniques
Handling large datasets securely and effectively
• Computational Tools and Platforms
Software and tools for implementing AI in drug discovery
Cloud computing solutions and infrastructure
• Ethical and Regulatory Considerations
Navigating ethical issues in AI-driven research
Understanding regulatory landscapes and compliance
• Future Directions and Emerging Trends
Ongoing research and development in AI for drug discovery
Predictive analytics and the future of pharmaceutical research
• Case Studies and Real-World Applications
Analysis of case studies presented by Richard Bonneau
Discussion on lessons learned and best practices
• Conclusion and Next Steps
Recap of key learnings
Practical guidance for implementing AI in drug development careers
• Additional Resources
Recommended readings
Online courses and certification programs for further learning

Subjects

Computer Science