Recent advances in large language models now enable reliable multi-step reasoning, marking a paradigm shift from AI-assisted coding to true agentic programming in real-world data science analyses. This session demonstrates how we successfully piloted this approach in RWD studies, using a domain-specific agentic workflow to transform research objectives into structured, production-quality R-codebases. Ultimately, this delivers a step-change in time to insight while shifting human effort from manual coding to scientific oversight.

A nonlinear dynamics and bifurcation-based framework for modeling complex medical systems connects mechanistic theory with modern machine learning. This approach enhances interpretability and prediction across multimodal data streams. A software package is presented that enables practical, scalable, and MLOps-ready implementation of these methods in biomedical data science.

Trustworthy AI and advanced analytics are only as reliable as the data ecosystems and infrastructure that feed them. While the "black box" of complex modeling is a known challenge, the "dark data" problem is the primary barrier to operationalizing research at scale. This session explores how to transform petabyte-scale unstructured data into validated, audit-ready assets. By integrating metadata extraction, lineage tracking, and AI-based PII detection directly into the data management layer, organizations can deliver data integrity and provenance across the research lifecycle. We will discuss how organizations can transform fragmented "dark" research data into validated, reproducible science, enabling compliant, interpretable pipelines that bridge the gap between raw data chaos and analytical clarity.

• How are knowledge graphs supporting reliable scientific intelligence
• Lessons from early deployments
• Remaining challenges and barriers to scale​

• Learn how leading global pharma companies are applying quantum principles to real-world research. 
• Discover the quantum companies transforming traditional processes around drug development and drug discovery.
• Discuss how quantum, along with HPC and AI, is poised to help researchers tackle historically intractable problems and potentially find new treatments for diseases like cancer, Alzheimer’s, and diabetes.​

• Where are we today? Are AI tools truly integrated into workflows, or still operating in silos?
• What changes with agents? How do LLM-based agents shift drug discovery from prediction to decision-making?
• What is blocking autonomy? Data quality, validation, trust, or organizational readiness for AI-driven discovery?​

• Where are the highest-value collaboration opportunities between academia/TTOs and industry (biopharma, startups, AI/data, investors), and what makes them actually work?
• What types of partnerships are most effective in practice (licensing, co-development, startup creation, sponsored research), and how do both sides evaluate quality and fit quickly?
• What specific formats, access points, or structures at the Bio-IT event would actually drive follow-up, partnerships, and deal-making?

• How do CIOs enable rapid AI and data innovation without breaking governance, compliance, or data integrity frameworks?
• What separates pilot-stage innovation from scalable, production-grade systems? Where do most organizations fail?
• How do leaders manage emerging risks (model bias, data provenance, regulatory exposure) while still pushing competitive advantage?
  

• How real-world data is being used alongside clinical and experimental data
• Challenges in standardization, access, and regulatory acceptance
• Opportunities to improve outcomes, access, and long-term patient insights

• Why critical experimental knowledge is lost across ELNs, LIMS, and siloed teams, and the cost of reinventing work 
• How structured experimental memory makes past decisions, failures, and context reusable across teams
• How organizations can make scientific knowledge reusable for both humans and AI

• How to think strategically about IP early in biotech and AI-driven research
• Common pitfalls in patent and trademark applications and how to avoid them
• Navigating ownership, protection, and commercialization of innovations
  

• Why do current workflow systems fail most scientists in practice? 
• Can no-code and AI-driven workflows meet standards for reproducibility, validation, and clinical readiness? 
• What would a “production-ready” workflow ecosystem actually look like across teams and organizations?

• The gap between AI ambition and the reality of data foundations, platforms, and operating models
• What “AI‑ready data” actually means in practice—beyond pilots, dashboards, and proofs of concept 
• Where AI initiatives most often break down: data integration, data quality, metadata, and governance at scale​

Pharmacokinetic (PK) modeling is widely used in drug development, yet its impact is often limited by inconsistent integration into decision workflows. This case study presents a production-ready data science framework that operationalizes PK-driven predictive models alongside Intuence Discovery to support compound selection and portfolio decisions. By embedding validated models into cross-functional workflows, teams improved predictability, reduced late-stage attrition, and accelerated decision-making across discovery and development.

We present a privacy-preserving approach using federated averaging to train convolutional neural networks on small-scale molecular datasets where data cannot be moved or shared. By simulating a multi-center environment with publicly available phenotype-linked omics data, we evaluate how varying site participation and model reuse affect performance—demonstrating that federated learning can improve generalizability and robustness even in low-data, distributed biomedical research settings.