NVIDIA's expanding role as infrastructure provider to Thermo Fisher and Eli Lilly is anchoring a wave of AI-native drug discovery platforms launched in rapid succession in early-to-mid 2026.1 BioNeMo, MindWalk's HYFT and ReefIQ, Boltz Lab, Owkin, Natera, Basecamp Research and Edison Scientific all emerged in the same window, each built to run GPU-heavy models against biological data at scale.
MindWalk gave the first public demonstration of its ReefIQ platform in July 2026.1 The system runs on HYFT Technology, a proprietary representation of biology built from roughly 660 million biological patterns — fingerprints encoding conserved relationships between sequence, structure, and function.3 MindWalk's argument is that representing biology accurately, not running the models themselves, is the harder computational problem and the place to build a lasting edge.3
The company backed that position with intellectual property. Patent EP26187897.9, filed in July 2026, adds a computational layer to MindWalk's foundational HYFT patent (EP3881326A1), organizing biological meaning around identified patterns for reuse across infrastructure, customer programs, and AI workflows.2 MindWalk describes it as a layer built on the original foundation, not a re-filing.2
The underlying problem these platforms target is data fragmentation. Biological data generated during drug discovery is scattered across files, formats, systems, and teams. Before an AI system can reason about a biological question, MindWalk argues, that information has to be reconnected into a single, governed, queryable web of context.1 That reconnection work is what GPU infrastructure from providers like NVIDIA is now being built to support at scale.
Established pharma is responding by shifting capability outward rather than building it in-house. Novo Nordisk posted strong Q1 2026 earnings and a rising stock while restructuring its own R&D, closing its internal cell therapy unit and licensing the asset to Cellular Intelligence, an AI-native platform, instead of continuing the work internally.4 The move signals that even top-tier pharma is favoring external AI specialists running on GPU infrastructure over maintaining in-house discovery pipelines.