In this webinar, learn how AI technologies can accelerate oncology drug discovery and enhance the success of anti-cancer agents.
Artificial intelligence is revolutionizing drug discovery as we know it, effectively replacing many high throughput screening studies with computer-generated insights. By integrating predictive AI and machine learning (ML) technologies with in vivo validation, researchers can gain swift and precise drug response insights. Whether you seek to understand a candidate drug molecule’s mechanism of action or explore potential drug synergies, this approach can bring clear and compelling evidence of therapeutic efficacy to your oncology development program.
In this webinar, learn how integrating predictive AI, ML, and advanced cancer models into your drug discovery workflow may give your most promising anti-cancer agents the best chance at translational success.
Key Learning Objectives
- Discover all the ways predictive AI can be applied to accelerate oncology drug discovery and beyond.
- Explore best practices for predictive AI platforms.
- Delve into new preclinical and clinical case studies that pair predictive AI with in vivo validation for more insightful drug response and synergy data.
- Experience our new virtual assistant tool in action — it uses natural language processing (NLP) to easily find tumor models with your desired genetic profiles.
Presenters
Senior Director
Scientific Engagement and Key Accounts
Certis Oncology Solutions