AI Drug Discovery Is Triggering a Biotech M&A Supercycle

The New Era of AI-Driven Biotech

The pharmaceutical industry was initially built on the prowess of the chemical industry, which started to create ever more useful products, including those for medical purposes. At first, it was mostly about isolating and purifying natural chemical compounds like aspirin from willow bark and quinine from a tropical tree.

Then it started to create entirely new compounds that never existed in nature and turned them into medicine. This approach has, however, started to fail in the past decades.

The easiest-to-produce or easiest-to-discover chemicals have already been found, and other molecules are often not good enough: too unstable, too toxic, too hard to manufacture, etc.

As a result, the pharmaceutical industry has turned to biotech, which repurposes biological molecules like hormones, proteins, or DNA & RNA into medicine. This yielded artificial insulin, monoclonal therapies, gene therapies, and many other impressive advances.

However, here too progress has started to stall, as the low-hanging fruit has been picked: most active biologicals are already identified and known, leaving the more complex, hard-to-figure-out biological mechanisms to cure diseases still resistant to treatment.

This is a serious problem for large pharmaceutical companies. Not only are many of their chemistry-based treatments out of patent protection or getting there soon, but the strategy to just buy or partner with biotech startups is not enough anymore.

These companies need to buy innovation fast, and what worked before is just not cutting it.

In large part, this is because a true representation of a single human cell would be almost incomprehensibly complex to a single person, as illustrated by a computer-generated picture of all the components of a single human cell that went viral a few years ago.

Source: Newsweek

Luckily, a new wave of biotech innovation is coming from the deployment of AI into biolabs. This comes in combination with a revolution in the data available from the so-called “multiomics revolution“, which creates an unprecedented quantity of data down to the intracellular level.

And AI, with its capacity for large dataset analysis going way beyond that of a human mind, is now helping make sense of it all.

Source: World Economic Forum

Illustrative of this trend is the partnership announced on January 12^th, 2026, between Nvidia (NVDA -0.16%) and Eli Lilly (LLY +0.26%), planning to jointly invest up to $1 billion over five years in infrastructure and research for AI drug discovery.

The Digital Biology Era

Digital biology is changing where time, cost, and failure risk accrue across the drug discovery pipeline—shifting more exploration and optimization into computation before the most expensive wet-lab work begins.

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The Rise of Multiomics

The sheer complexity of living systems has led to the emergence of multiomics, a field merging together all the -omics sub-segments of biological sciences and touted as the next step in biotechnology:

• Genomics: the analysis of the DNA sequence in the cells’ nucleus.
• Transcriptomics: the analysis of mRNA carrying the DNA’s instructions.
• Epigenomics: the modification of the genome without affecting the genetic sequence, or “epigenetics”.
• Proteomics: the analysis of proteins, including the modification of proteins with sugars (“post-translational”).
• Metabolomics: the analysis of chemical compounds and metabolism.
• Microbiomics: the analysis of all the microbes living inside or on the body.
• Single-cell multiomics: the multiomics analysis on individual cells.
• Spatial biology: analyzing in 3D the location of specific mRNA, proteins, or cells.

Source: Ark Research

Multiomics also emerged thanks to much more powerful analytical tools, from genetic sequencers to spatial biology.

The issue is, however, that this generates such a flood of data that, for the first time, the issue for biologists is not to finally find an interesting data point to use for practical applications, but to decide what data is actually relevant to a given problem.

If every newborn in the world had its genome sequenced, a likely practice in the coming years, this would generate 10,000x the data used by an AI like Llama every year.

Source: Ark Research

What Is Digital Biology?

A new option for biotech research had recently appeared: the in-silico approach, where one or several virtual cells are simulated in a computer.

“In 2026, identifying disease targets will rely on in silico exploration before any wet-lab validation begins.

This will reduce the number of programs that stall during preclinical development.

Veronica DeFelice – Director of Biologics at Sapio Sciences.”

These virtual cells are then exposed to the potential new treatment, and the simulation calculates how they would react.

Source: Ark Research

Another option is to simulate the 3D configuration of a protein, which ultimately determines its biological function.

A protein folding simulator like Google’s AI AlphaFold (GOOGL -0.06%) has improved by up to 500x since 2018.

Source: Ark Research

So it is likely that in-silico simulations will become a must-have technology for most pharmaceutical & biotech companies.

Another form of digital biology is the use of an advanced detection system to identify markers of cancer in a blood sample, replacing costly and less efficient biopsies, leading to earlier discovery of potential cancers.

Lastly, automation, robotics, and AI are combining to create automated labs that can run experiments without human labor, and check millions of potentially useful molecules or compounds at a lower cost and 100x the speed of traditional research methods.

Investing in AI Drug Discovery

Eli Lilly Overview

Eli Lilly is a massive pharmaceutical company that built its medical empire on quinine and then insulin.

Diabetes treatment has remained the core of the company, with a long series of molecules discovered and approved in the past 3 decades for this disease. This included tirzepatide, commercialized under the Mounjaro brand name.

In the 2010s, Eli Lilly also went big in oncology (cancer treatments) with a series of acquisitions and partnerships in the field, as well as in-house drug development efforts to build a strong portfolio of oncology drugs.

The tirzepatide molecule has since been repurposed as an anti-obesity drug, under the brand name Zepbound, the largest competitor to Ozempic. This has been a lifesaver for Eli Lilly, as many of its older drugs went off patent, meaning that generic drug manufacturers can now produce them too, and compete on price.

Still, relying massively on a single patented peptide and its potential derivatives is a risky position and not sustainable in the long term. Like most pharmaceutical companies, there is a dire need to return to diversified innovation. But unlike many others in the industry, Eli Lilly has a plan, and it relies on going all-in on AI & digital biology.

Eli Lilly AI Drug Discovery

The largest and most recent announcement around AI drug discovery & Eli Lilly is its partnership with Nvidia.

“NVIDIA and Lilly are bringing together the best of our industries to invent a new blueprint for drug discovery — one where scientists can explore vast biological and chemical spaces in silico before a single molecule is made.”

Jensen Huang, founder and CEO of NVIDIA”

The plan will combine Lilly’s agentic wet labs with computational dry labs, enabling 24/7 AI-assisted experimentation. This combines with a previously announced AI supercomputer using 1,000 NVIDIA Blackwell Ultra GPUs for an AI factory that will train large biomedical foundation and frontier models for identifying, optimizing, and validating new molecules.

“Our foundation models are spawning new possibilities for our chemists, helping them uncover new motifs and configurations of atoms that were out of reach with traditional methods.”

Thomas Fuchs – Chief AI Officer at Eli Lilly”

This $1B partnership is just the latest (and largest) move of the pharmaceutical company into AI drug discovery. Previously, it notably:

• Entered a research collaboration with Insilico Medicine, with up to $100M to be paid to the startup depending on research milestones.
• Partnership with Circle Pharma to leverage Eli Lilly’s AIs to improve its macrocycle therapies, including for historically undruggable targets.
• Develop machine learning models with insitro, a pioneer in machine learning for drug discovery and development.
• Collaboration with OpenAI to discover novel medicines to treat drug-resistant bacteria.
• Using Genetic Leap’s AI models for the discovery of RNA-targeted drugs in a $409M deal.
• AI partnership with Google/Alphabet’s digital biotech company Isomorphic Labs, which will receive $45M upfront and is eligible for up to $1.7B in performance-based milestones.
• A deal worth up to $670M with Genesis Therapeutics for the company’s molecular AI platform.
• A $1.3B deal with Superluminal to discover obesity medicines using AI, thanks to its proprietary AI-driven platform targeting G-protein-coupled receptors (GPCR), with a goal to reinforce Eli Lilly’s already leading presence in the obesity market.
• A deal with BigHat Biosciences to advance the discovery of AI-driven antibody therapeutics thanks to its Milliner platform.

Eli Lilly also partnered with Benchling, a cloud-based software platform designed for life science R&D, to provide biotech startups with access to models trained on decades of Lilly’s proprietary research data.

Called TuneLab, this platform should help Eli Lilly partner with early-stage life sciences under its “Catalyze360” program.

Overall, these AI drug discovery partnerships and infrastructure building in all directions are likely to rebuild Eli Lilly’s discovery pipeline and boost its position in antibiotics, cancer drugs, rare diseases, and obesity.

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