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  Business Intellilgence

How 2026 Started: First-Weeks Readout on AI, Pharma, & Policy

by BiopharmaTrend   •   Feb. 6, 2026

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Early-year overview spanning virtual cell modeling, AI workflow plumbing in R&D and healthcare, obesity-driven capital and licensing, patent-cliff positioning, and FDA/EU policy signals.

The year opened hot, with the first weeks of January packed with deal flow, mega-rounds, platform launches, and AI model deployments as JPM week got underway. Companies doubled down on AI partnerships and infrastructure: for example, Eli Lilly and NVIDIA announced a $1 billion, five-year joint AI lab in San Francisco, aimed at making computational models core drug R&D infrastructure.

Efforts to amass massive “virtual biology” datasets also made news with Tahoe Therapeutics, Arc Institute, and Chan Zuckerberg Biohub combined roughly 120M single‑cell profiles and 225k perturbations into what they position as the largest open resource for virtual cell modeling. Illumina unveiled a Billion Cell Atlas, built with AstraZeneca, Merck, and Lilly, to train models on standardized cell-line experiments. Observers, though, kept a critical eye on translation, noting that cell lines are not patients and these curated perturbation signals may not cleanly map to real clinical biology. A smaller but distinctive example of the “data and population specificity” theme was BioMed X and the Barbados government’s project to model early diabetic kidney disease using deep molecular profiling and AI, framed as a population-specific “digital African twin.”

Pharma interest in virtual-cell-style modeling showed up in two directions: GSK signed a five-year, non-exclusive license and collaboration with Noetik, committing $50M to use its virtual cell foundation models and bespoke spatial oncology datasets in non-small cell lung cancer and colorectal cancer, while Bristol Myers Squibb entered a strategic clinical-development agreement with Immunai to apply its immune “operating system” to high-dimensional trial data to decode patient immune responses/variability in treatment outcomes.

Another clear theme was the continued movement of AI from proofs-of-concept into everyday workflows. Pharmaceutical players started bringing AI in-house and embedding it into R&D operations – AstraZeneca’s mid-month acquisition of Modella AI was explicitly to fold foundation models and “agentic” AI workflows throughout its oncology pipeline. On the tools side, MIT spinout Boltz (the founder trio is behind the well-known BoltzGen model) launched as a public benefit corporation with $28M seed round, and a multi-year collaboration with Pfizer.

Below is a selective rundown of the early-year signals we tracked most closely.

💊 AI in Drug Discovery

Among some of the notable platform partnerships and deployments, Isomorphic Labs partnered with Johnson & Johnson to apply its platform across multi-modality drug design in hard-to-drug indications, while pushing back its own AI-designed drug trials to end-2026. Servier also partnered with Iktos to use its AI-and-robotics DMTA platform for small-molecule discovery.

As for software integration, Insilico Medicine (IPO’d in Hong Kong with >$290M just at the end of last year) integrated its drug discovery foundation model into Microsoft Discovery and launched “Science MMAI Gym” to train and evaluate LLMs on pharma R&D tasks.

🔹 AI Drugs in Clinic — AI-originated assets continued to progress with several notable selective clinical touchpoints. Insilico Medicine advanced a gut-targeted PHD inhibitor into Phase II for ulcerative colitis, secured an IND for a brain‑penetrant NLRP3 inhibitor in CNS disease, and nominated a preclinical small molecule with Hisun eight months into collaboration. Lantern Pharma received a third orphan designation for its AI-designed LP‑284.

🔹 Post-AlphaFold — ByteDance, the Chinese company behind TikTok, introduced a protein folding system building on AlphaFold 3, alongside the emergence of “post-folding” approaches like DrugCLIP, which embeds proteins and small molecules into a shared representational space.

🔹 Against developability constraints — New generative models started to encode real-world constraints into sequence design: 1910’s PEGASUS is designed to target cell-permeable macrocyclic peptides, addressing a long-standing challenge in peptide drug development—designing molecules that retain favorable drug-like properties while still being able to enter cells; and Absci’s launched Origin-1, focused on de novo antibody design against difficult targets.

DeepMind’s AlphaGenome, first previewed in 2025, and models regulatory effects of non‑coding variants across long DNA windows, framing the AI tool potentially suitable for rare-disease variant interpretation and synthetic DNA design.

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