Modern computing and analytics platforms must deliver scalability to support diverse user communities, from regulatory submissions and patient safety in GxP environments to non-GxP use cases like exploratory analysis, all built on an adaptive, scalable infrastructure.This case study explores the journey of creating a validated, multi-lingual analytics ecosystem on a shared storage platform, leveraging insights from non-regulatory use cases. It highlights key architectural decisions, governance and validation strategies, and operational lessons, providing practical guidance for designing scalable, compliant analytics platforms in regulated environments.

In clinical biostatistics, reproducibility isn't optional—it's a regulatory mandate. This presentation details the architecture of a truly reproducible R workflow designed for GxP-compliant clinical analysis. We will break down our solution into four key pillars—Code (Git), Dependencies (Posit Package Manager), Environment (Docker), and Execution (CI/CD)—demonstrating how they combine to create a validated, auditable, and automated platform that ensures the integrity of every result and streamlines regulatory submissions.

NCI's Cancer Research Data Commons is a data ecosystem built to administer and manage the data generated by the various NCI funded programs for FAIR data sharing across the research community. NCI has been a pioneer in establishing a democratized ecosystem co-locating data and compute, building secure and scalable data commons and cloud analytical platforms for the diverse users from cancer community.

In this session, we will discuss a federated data architecture and operating model to shift ownership of data to domain teams and treat datasets as discoverable, secure products—enabling data producers to publish well-documented, versioned data products, data consumers to self-serve high-quality assets for analytics and ML, and platform teams to provide the plumbing, tooling, and federated governance that makes it all scalable. By combining product thinking, domain ownership, an organization-wide data catalog, and platform-provided capabilities (cataloging, lineage, observability, automated quality checks, and data contracts), we have made pipelines resilient to sudden schema changes, elevated accountability for data quality, and removed the central-team bottleneck that traditionally limited access. The result: faster time-to-insight, more reliable data for data science and analytics, and a governed, interoperable ecosystem where standards and policy are enforced without stifling domain innovation, demonstrating how a pragmatic federated implementation can deliver both autonomy and enterprise-grade control.

MATRIX (Montai AtTRIbute eXpander) system is a cloud-native platform for large-scale molecular property generation with full data traceability. It produces 50+ attributes for 500M compounds in under two hours, addressing throughput and reproducibility challenges in biotech R&D while enabling faster, more reliable discovery workflows.This scalable, reproducible workflow modernizes cheminformatics and provides a blueprint for rapid, traceable, research-grade bioinformatics discovery.

As demand for analytics and AI accelerates, many organizations face a hidden bottleneck: they don’t know what data they have, where it lives, or how quickly it can be made usable. While FAIR principles define what good data should look like, they don’t solve the operational challenge of discovering, curating, and serving real-world datasets across fragmented storage environments. This session explores the emerging concept of a data facility—an evolution beyond file systems, catalogs, or data lakes. Examine how organizations can architect platforms that actively surface valuable datasets, connect distributed storage, apply readiness workflows, and deliver data that is truly “compute-ready” for downstream research.  Attendees will learn practical approaches for automating data discovery, reducing reliance on human intermediaries, and building infrastructure that turns legacy and siloed data into reusable research assets.

Metaphore Bio is building a function-first generative drug-design platform where data infrastructure is a first-class product. Cloud-orchestrated autonomous labs stream high-throughput functional and multiomic data into a governed cloud platform that powers AI/ML and protein design at scale. I will share how our storage and compute architecture balances speed, performance, cost, and compliance while accelerating functional biologics.

Lilly is developing an integrated digital platform to transform large molecule discovery in the age of AI. From assay data capture and NGS workflows to MLOps and DMTA, the solutions in this platform are unified in both data and UI by design. This talk will highlight the roadmap, progress, impact, challenges, and learnings from this transformational platform.

Structure Designer is an internally built, customizable application for AbbVie's Discovery medicinal chemists, centralizing compound design workflows and data access, and replacing fragmented, vendor-dependent solutions with a flexible, web-based platform. Its modern architecture integrates rapid calculations, seamless session management, and secure user controls, significantly improving speed, usability, and accessibility for both internal staff and external collaborators, all at a lower cost than commercial alternatives.

For 20 years, Trends from the Trenches has been Bio-IT World’s unscripted pulse check, offering candid, insider perspectives on what works, what fails, and what’s pure hype in scientific computing. As the field evolved, the session broadened to reflect the real operational challenges and breakthroughs shaping R&D. For 2026, the format evolves again: a focused, credibility-driven keynote paired with a community-powered unconference built from attendee input. The result is a forum for late-breaking insights, grounded realities, forward-looking perspectives, and practical solutions you won’t find in vendor decks, marketing summaries, or any LLM. It leaves participants energized by the collective intelligence in the room and inspired by the emerging possibilities shaping the future of life-science computing.

This presentation frames the industry’s next chapter by tracing how Trends from the Trenches has shaped digital R&D for two decades and by spotlighting the forces redefining scientific computing today: AI–HPC convergence, modality-driven compute, multimodal data, and rising expectations for speed, interoperability, and trust. Remarks set the foundation for a forward-looking exploration of where digital biology and computational innovation are heading next.

This session features a structured, participatory unconference built around topics sourced from Bio-IT event attendees during the conference week. A working group synthesizes all submissions Wednesday evening into a small set of high-value discussion themes. The facilitator guides the room through rapid-fire exchanges, micro-debates, and collaborative problem-solving focused on operational realities in AI, computing, data engineering, and scientific software. The goal is to surface patterns, stress-test ideas, and extract practical solutions emerging across the community—creating an annual, crowd-generated state-of-the-field snapshot that only this session can produce.