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From model to molecule: Combining AI and experimental strategies to transform drug development
On-demand webinar
AI-driven predictive modeling is advancing drug development in many ways. However, computational methods still require experimental validation to confirm their accuracy and reliability—and they may not be applicable or effective in all areas.
To maintain steady progress, a carefully planned and well-executed experimental approach is essential. This is especially important when evaluating the solid-state properties of active pharmaceutical ingredients (APIs), which are fundamental to rational drug product development.
Our recent webinar featured real-world case studies illustrating how the integration of computational and experimental strategies can help overcome challenges, improve decision-making, and drive transformative results in drug development.
Key takeaways:
- Addressing limitations when computational methods are unavailable or unreliable
- Integrating predictive modeling and experimental validation to achieve accurate outcomes
- Key considerations for assessing the solid-state properties of APIs in drug development