Executive Summary
Drug discovery doesn’t stall on ideas; it stalls at the hand-offs, data scattered across teams and locations, results rerun because the originals weren’t saved, and expensive GPUs waiting for files to arrive. NVIDIA® BioNeMo™ fixes the science side by giving you ready-made “engines” for the hard steps: it lines up a protein with many similar ones in nature (multiple sequence alignment, a way to spot the parts that matter), predicts its 3-D shape, suggests new small molecules that might bind, and then checks how well each one fits in the pocket.
DDN Infinia fixes the operations side: it is the federated data backbone where every result is saved once and reused everywhere, with clear who-did-what-when history, permissions that match roles, and privacy enforced across on-prem and cloud. Net: fewer dead ends, GPUs that stay busy, and a steady flow of defensible candidates your program teams—and auditors—can act on in hours, not months.
For the last year we’ve been hardening a GPU-native discovery pipeline with NVIDIA that turns disjointed model runs into a repeatable, production-grade system. Think of it as two synchronized engines sharing one data backbone:
- A Virtual Screening Engine that goes sequence → structure → docking → ranked candidates at GPU speed.
- A Discovery Intelligence Engine (RAG for science) that turns literature, reports, and internal notes into grounded answers and decision support.
The secret is simple: keep every intermediate artifact and every model output on a single, high-performance data plane so GPUs never stall and scientists never repeat work. That plane is DDN Infinia. It is the system of record (everything we compute is persisted) and the system of motion (everything the models need streams directly, locally, fast). Infinia connects the screening and reasoning paths and eliminates “copy-and-pray” I/O that kills utilization.
The Architecture
This architecture shows a unified drug discovery pipeline that combines physics-based virtual screening with LLM-driven scientific reasoning, all anchored by a high-performance data backbone.
On the left, the Virtual Screening Pipeline transforms raw biological and chemical inputs into ranked drug candidates. Protein sequences flow through MSA-Search and OpenFold2 to generate protein structures, while GenMol produces novel molecules. DiffDock then performs structure-aware docking, producing scored binding poses and ranked candidates. Every intermediate artifact—structures, molecules, poses—is persisted to Infinia S3, enabling reuse, iteration, and auditability.
On the right, the RAG Discovery Pipeline turns unstructured scientific knowledge into actionable context. Literature and reports are ingested with NVIDIA® Ingest (nv-ingest) , embedded and indexed using Milvus + cuVS, and queried by NVIDIA® Nemotron™ LLM. This allows scientists to ask natural-language questions and generate reports that are grounded in both published research and the latest screening results.
This isn’t slideware. It’s an operational workflow you can deploy in days, compose with your instruments and Electronic Lab Notebooks/Scientific Data Management Systems (ELNs/SDMS), and scale across sites and clouds using the same federation model we’ve already proven in financial services: one logical data substrate, many compute domains, policy-driven movement only when necessary.
How the Pipeline Works (and How NVIDIA BioNeMo Fits)
NVIDIA BioNeMo gives us ready-to-use “engines” for the hard science steps—lining up a protein with similar ones in nature, predicting its 3-D shape, proposing new small molecules, testing how they fit, and explaining the results in normal language. Our pipeline wires those engines together so the work runs end-to-end, every result is saved, and nothing gets rerun or lost. DDN Infinia sits underneath as the data backbone that keeps GPUs busy and gives us clean lineage and access control.
Step-by-step (from sequence to decision):
- Add context (what nature already knows). We compare a target protein to many related proteins. That shows the parts that are conserved—usually the parts that matter.
- Predict the shape (the 3-D “lock”). Using that context, we predict a high-confidence 3-D structure for the protein—the lock the drug must fit.
- Propose and test molecules (the “keys”). We generate many candidate molecules, “dock” them into the 3-D structure, and score how well each one fits.
- Explain the choice (no black boxes). In parallel, we pull in papers and reports, search them, and have a science-tuned LLM draft a short, cited explanation: why this target, why this molecule, what to do next.
- Persist everything (so we never redo work). Every alignment, structure, molecule, score, and report is saved once in Infinia – versioned, searchable, permissioned. You can re-rank or re-explain without rerunning upstream steps.
Bottom line: NVIDIABioNeMo supplies the horsepower; the pipeline and Infinia turn it into a repeatable factory with audit-ready outputs.
Why “Alignment + Structure” Really Moves the Needle
Most discovery programs lose time and money because the context and the structure are weak – and everything downstream suffers.
- Comparing the protein to many relatives (alignment) isn’t optional. It highlights the important regions and makes the shape prediction better. Skipping it is a false economy that leads to more wet-lab dead ends.
- Modern structure models (AlphaFold/OpenFold-class) use that context to produce reliable 3-D shapes fast. Better shapes mean the “fit tests” for candidate molecules are more meaningful, so you kill bad ideas earlier and cheaper.
NVIDIA BioNeMo bundles these steps into GPU-native services, so you get speed and consistency. When the science improves (new AlphaFold releases, better generators or docking), the engines update without breaking your workflow. Your teams keep moving; the platform keeps getting faster.
What this means for an executive: fewer reruns, fewer false positives, earlier yes/no decisions, and a pipeline that turns time and spend into defendable candidates – not slideware.
Why Infinia Is the Difference Between a Demo and a Program
Chaining models isn’t enough—you still lose days to slow data hand-offs, missing outputs, and GPUs waiting for files. Infinia fixes the substrate so the science actually moves at business speed:
- One source of truth. Every result—alignment, structure, molecule, docking score, report—is saved once and reused, so nothing gets rerun or lost.
- Data that keeps GPUs hot. Models read and write in place at wire-speed; utilization climbs from ~60–70% to 90%+.
- Provenance and control. Built-in lineage (who/what/when), role-based access, and policy-timed retention – so audits take hours, not weeks.
- Federation without drag. Run screening in one site and reasoning in another; move only the delta under policy—no forked datasets.
- Same experience on-prem and in cloud. One namespace across your GPU farms and NVIDIA BioNeMo services—no “lift-and-shift” rewrites.
Net effect: faster cycles from design–make–test in weeks → days, higher GPU efficiency, and evidence you can stand behind in every review.
What This Means for a Top-10 Biopharma
Large discovery engines share the same three drags:
- Waiting and redoing. Screening jobs queue up; teams rerun work because prior outputs weren’t saved.
- Scattered evidence. Assays in one place, structures in another, literature somewhere else—reviews crawl because the story isn’t in one view.
- Fragile hand-offs. Scripts and local tricks break between steps and sites; every group reinvents the wheel.
With the DDN and NVIDIA Architecture:
- Hours to structure, not days. Sequence → alignment → 3-D shape runs fast and stays saved – no recomputes.
- Continuous ranking. As docking finishes, the candidate list updates automatically; teams don’t wait for weekly batches.
- Days to design-make-test. Active-learning gates and automation cut cycle time to days, not weeks.
- One-click lineage. Pull the full trail for a top hit – sequence → alignment → structure → pose → cited evidence – in a single query.
You don’t need to rip and replace. We federate across your existing estates (on-prem, private, public cloud). Infinia names and governs the data; BioNeMo runs where performance and cost make sense. That’s how you scale globally without creating another generation of data drift – and how you turn time and spend into defensible candidates.
What You Get in 30/60/90 Days (CEO/CIO Lens)
Day 30 – Baseline live:
- One target through MSA → OpenFold → DiffDock, artifacts persisted in Infinia; literature RAG answering “why this target, what risks.”
- GPU utilization dashboard wired; lineage evidence for the first review.
Day 60 – Throughput and governance:
- Batch targets and library screens running; 90%+ GPU utilization typical; versioned structures/molecules searchable by program.
- Role-based policies active (program, site, partner); automated reports for MRM/audit.
Day 90 – Closed loop:
- Active-learning gates wired to wet-lab queue; first design-make-test cycles measured in days.
- Cost per decision and time-to-lead trending down; playbooks for expansion to additional therapeutic areas.
Why This Matters Beyond One Pipeline
Put data at the center and make compute go to the data – and the tax of glue code, reruns, and hand-offs disappears. With NVIDIA’s engines on a DDN Infinia backbone, discovery moves from weeks to days, costs fall, and evidence is audit-ready by design. The AI pipelines that our team has created is already transforming FSI risk, healthcare imaging, and real-time analytics. In drug discovery the stakes are higher: we’re compressing the distance from science to therapy. Do that across Merck-scale portfolios and you don’t just ship candidates faster—you shift the curve of human health.
