ウェビナー動画：Novartis社、Genedataのグローバルな導入展開から得た教訓 - 自動化・新規モダリティ・AI/MLへの影響
Novartis社では、配列/構造/アッセイデータを統合・構造化することで、抗体の開発可能性を予測するモデルを構築し、創薬初期でのデータ駆動型の意思決定を実現しています。これにより、開発期間の短縮、分子品質の向上、そしてAIを用いた研究開発へのスケーラブルな対応が可能になります。
In this webinar, Kannan Sankar from Novartis shares how the Biologics Research Center is leveraging Genedata Biologics to transform biologics discovery through structured data capture, AI/ML modeling, and predictive analytics. The session explores how Novartis integrates molecule and assay data to build models that predict developability of antibody-like molecules, enabling faster, smarter decision-making in early-stage drug development. Learn how sequence-structure-function relationships are being used to accelerate timelines, improve molecule quality, and prepare for scalable AI-driven R&D.
Key Learning Objectives
- Enable predictive modeling of antibody developability using sequence and structure-based features.
- 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.
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.