Variations in scientific research processes have resulted in data being dispersed across multiple siloed systems at Bristol Myers Squibb. By leveraging the capabilities of knowledge graphs, we are able to seamlessly integrate these previously isolated data sources into a unified, contextualized framework. This enriched data model empowers scientific subject matter experts in product development to discover new insights, foster greater collaboration, and accelerate innovation. Ultimately, this approach enables more informed decision-making and advances BMS’s product development capabilities.

Biomedical discovery and research generate enormous volumes of knowledge, yet much of it remains locked in hard-to-find PowerPoint slides, fragmented analyses, and disconnected data systems. As teams evolve, critical context around what was done, why decisions were made, and which data and models informed them is often lost. This presentation outlines an end-to-end knowledge management capability that spans discovery and research, leveraging knowledge graphs, ontologies, and generative AI to link unstructured content with analytical models and underlying raw data. By integrating directly with workflows, collaboration tools, and decision-support systems, this approach transforms knowledge from static artifacts into a continuously accessible, reusable, and decision-ready asset.

• 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​

Drug discovery requires integrating diverse biological, chemical, and genomic data, yet the benefits of robust FAIR practices are often less visible than the effort required to implement them. BioRels changes this by making FAIR both effortless and impactful. Its automated, schema-driven infrastructure standardizes incoming sources, harmonizes ontologies, captures full provenance, and enables reproducible reconstruction across billions of data points. By turning data preparation into a fast, reusable, and transparent process, BioRels removes friction and accelerates key discovery activities—from target identification and validation to mechanism exploration and AI enablement.

In a chemical registration system, the validation and standardization of submitted chemical structures are crucial for ensuring that the deposited representation of registered compounds is accurate, unambiguous, and compliant with internally defined business rules and QA criteria. This aspect of the registration process not only plays a central role in identifying registered chemicals but also significantly contributes to the quality and reliability of data available to downstream applications and users. Over time, for a system already in production, the evolution of validation criteria and standardization rules and their adaptation to evolving requirements necessitate careful consideration and a structured approach to implementation and testing. This presentation discusses recent advancements in the Small Molecule Registration (SMR) product used at Novartis Biomedical Research. It highlights the replacement of the original validation and standardization component with a newly implemented module, which was subsequently published as open-source code and contributed to the RDKit cheminformatics toolkit.

Based on extensive research, this presentation explores the three leading ways AI can accelerate the drug development process while substantially reducing costs. We provide a framework for considering how to deploy these capabilities in research and clinical while setting out (i.) the supporting evidence base and (ii.) the remaining challenges to be overcome. Our priority is to provide the audience with practical ways to positively impact their own drug development processes.