Webinar
The Next Wave of AI: the Impact of LQMs on the Pharmaceutical Industry
Join Fierce and SandboxAQ as we explore cutting-edge technology that is pioneering the next wave of AI.
As the pharmaceutical landscape evolves, the need for innovative solutions to traditional challenges becomes paramount. This session will explain how Large Quantitative Models (LQMs) are redefining the drug discovery process, enabling researchers to navigate the complexity of the chemical space with unprecedented speed and accuracy.
This webinar is a can’t-miss opportunity for professionals who are eager to learn about the future of drug discovery and the role of AI in shaping it. Register today.
Webinar Takeaways
- Introduction to LQMs: Learn about Large Quantitative Models and their role in simulating intricate biological behaviors. Discover how these models are complementary and maximize the impact of expensive, time-intensive physical experiments, significantly accelerating the discovery timeline.
- Understanding the challenge of the traditional drug discovery framework and the blockers that slow down innovation.
- We will discuss the types of data that power LQMs, including molecular simulations, rigorous physics-based methods, foundational methods, and their integration into generative AI solutions.
- Learn how LQM technologies can enhance protein target identification, rational drug design, structure-based methods, pharmacokinetics, and pharmacodynamics properties faster than traditional methodologies, streamlining the clinical candidate identification process.
- Explore how AI analyzes biological data using LQM powered by effective knowledge graph solutions, to identify potential drug targets and predict compound activity, thus reducing screening costs and optimizing lead compounds through advanced structure-activity relationship modeling. A compelling case study will illustrate these advancements in practice.
- Applications of LQM in drug development, knowledge graph-based LQM and patient selection in clinical trial development. Address critical considerations surrounding data privacy, ethical implications, and regulatory compliance, highlighting the necessity of integrating AI with existing workflows.
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