From model to molecule: Combining AI and experimental strategies to transform drug development

Artificial intelligence (AI)-driven predictive modeling is advancing pharmaceutical development by providing innovative tools to optimize workflows and reduce timelines.

However, these computational methods still require experimental validation to confirm their accuracy and reliability, and they may not be applicable or effective in areas where methods are unavailable or inaccurate.

To ensure steady progress within a reasonable timeframe, a carefully planned and well-executed experimental approach is critical. This is particularly important when evaluating the solid-state properties of active pharmaceutical ingredients (APIs), which are foundational for rational drug product development.

This webinar will use real-world case studies to highlight how integrating computational and experimental strategies can help overcome challenges, improve decision-making, and drive transformative results in drug product development.

Participants will learn:

• Strategies to address limitations when computational methods are unavailable or unreliable.
• How to combine predictive modeling and experimental validation to accelerate development and achieve reliable outcomes.
• Critical considerations for determining the solid-state properties of APIs to enable rational drug product development.

Watch On-Demand →