Lessons Learned from a Global Genedata Deployment: Impact on Automation, Novel Modalities, and AI/ML
In this webinar, Kannan Sankar from Novartis shares how the Biologics Research Center is transforming biologics discovery through structured data capture, AI/ML modeling, and predictive analytics. This work reflects the broader shift toward AI biopharma research and development, where unified data, automation, and intelligent computational methods are reshaping how scientists evaluate and optimize next‑generation therapeutics. By integrating molecule and assay data into a centralized platform, Novartis is building the foundation for scalable ai drug discovery and more informed early‑stage decision‑making.
A key enabler of this transformation is the organization’s commitment to FAIR data, AI data integrity, and strong AI data governance aligned with ALCOA+ principles. These practices ensure that datasets are complete, traceable, and ready for advanced modeling. With the support of AI cloud solutions, Novartis can harmonize multimodal data across teams, streamline biologics discovery workflows, and bridge wet‑lab and in silico processes. This creates an environment where predictive models can be continuously developed, validated, and improved, accelerating timelines and enhancing molecule quality.
Kannan also highlights how these capabilities advance AI in scientific research and development, enabling scientists to explore sequence‑structure‑function relationships with greater depth and precision. As predictive modeling becomes more deeply embedded in R&D, it strengthens the strategic impact of AI in biopharma, empowering researchers with data‑driven insights for molecule selection and optimization. This webinar offers a clear view into how Novartis is building an AI‑ready discovery engine—one that supports automation, robust data governance, and the next generation of intelligent biologics development.
Key Learning Objectives
- Integrate molecule and assay data to support AI/ML model development and validation.
- Streamline biologics discovery workflows by centralizing data and enabling automation.
- Bridge wet‑lab and in silico workflows to reduce development timelines and improve molecule quality.
- Support continuous model evaluation by incorporating in silico predictions into the data platform.
- Empower scientists with tools for data‑driven decision‑making and molecule selection.
- Enable predictive modeling of antibody developability using sequence‑ and structure‑based features.
Who Should Watch
- R&D Scientists and Innovation Leaders: Gain insights into predictive modeling and data integration strategies that accelerate biologics discovery.
- Data Scientists, AI/ML Specialists, and Bioinformatics Teams: Explore how to extract and use sequence and structure-based features to build accurate models for molecule developability.
- IT, Informatics, and Scientific Software Developers: Learn how Novartis connects structured informatics tools, AI frameworks, and Genedata Biologics to enable scalable infrastructure.
- Innovation Managers and Program Leads: Understand how AI and digital tools can deliver measurable impact in early-stage drug development.
- Regulatory, Quality, and Data Governance Experts: See how structured data capture and traceability support compliance and IP protection.
- Biotech Executives and Collaborators: Evaluate the strategic value of integrating predictive modeling into biologics R&D.