"Beyond the Promise of AI: Deploying Impactful AI into purposedly-built Data Infrastructure combined with experimental confirmation to enable an innovative Risk-Sharing Model"
Charles River Laboratories, Baden-Wurttemberg, Germany
The potential of AI in drug R&D has been widely celebrated, yet skepticism arises from its modest impact on efficiency and success rates to date. Our presentation addresses this by illustrating how a specifically designed data infrastructure, comprehensive experimental data, and stringent lab validations can reverse this trend. By seamlessly integrating AI modeling with laboratory testing with the aim of globally optimizing the 'design-make-test-analyze' cycle, we accelerate models development and refine compounds in parallel. This approach shifts the discovery paradigm towards achieving critical value milestones, enabling a collaborative model that mitigates risk for pharma and champions joint scientific discovery. Our method utilizes a comprehensive data infrastructure, avoiding siloes, to enhance model accuracy, allowing for earlier computational predictions than what is typically achieved by current AI methods embedded in conventional discovery approaches. By integrating this robust data framework with rapid laboratory testing, we facilitate quicker hypothesis validation and drug development progress. We aim to set a new industry standard, improving timelines while adopting a more judicious approach to animal research. Join us to discover how Logica's fusion of data science and empirical research is not only making AI in drug R&D accessible but also accountable, offering a substantiated route to accelerate drug programs.