Chai Discovery company profile: valuation, funding & revenue

This Chai Discovery company profile covers the company's valuation, revenue, funding rounds, investors and history. Chai Discovery works in AI, with a focus on drug discovery. It was founded in 2024 and is headquartered in San Francisco, California, United States. Its latest valuation on record is US$1.3 billion (December 2025). Every figure links to a public source, and anything the company hasn't disclosed is marked as such.

Revenue

Funding history

Chai Discovery timeline

Key dates from the sources on this page, oldest first. Metric snapshots are listed after the funding events.

• 2024 — Chai Discovery is founded in San Francisco by Joshua Meier (CEO), Jack Dent and co-founders.
• Dec 2025 — Series B: US$130M, valuing the company at US$1.3B. Investors: Oak HC/FT and General Catalyst (co-leads).

Investors

Overview

Chai Discovery builds AI models that predict and design the molecular interactions at the heart of drug discovery. Its models aim to predict how proteins, small molecules, antibodies and other molecules fold and bind together, and increasingly to design new molecules, especially antibodies, that bind specific disease targets. The goal is to replace parts of slow, expensive laboratory screening with computational design, so drug developers can reach promising candidates faster.

The company was founded in 2024 in San Francisco by a team with backgrounds in AI research, protein modelling and software engineering, including chief executive Joshua Meier, who previously worked on protein language models at Meta's AI research group. OpenAI was an early investor. In December 2025 Chai raised a US$130 million Series B co-led by Oak HC/FT and General Catalyst at a US$1.3 billion valuation, according to Fierce Biotech, making it one of the most valuable young AI drug discovery companies.

Products and models

• Chai-1: an open model released in 2024 for predicting the 3D structures of molecular complexes, including proteins, small molecules, DNA and RNA.
• Chai-2: a model focused on designing antibodies from scratch that bind chosen targets, which the company reported achieved high laboratory success rates in early tests.
• Design platform: software and services for pharmaceutical and biotech partners to apply Chai's models to their programmes.

Funding

Chai raised seed funding from investors including Thrive Capital and OpenAI, followed by a Series A led by Menlo Ventures in 2025. Its December 2025 Series B of US$130 million was co-led by Oak HC/FT and General Catalyst and valued the company at US$1.3 billion, according to Fierce Biotech. The company said it would use the money to expand its research team, improve its models and work with drug developers.

Business model

Chai's commercial path is likely to include partnerships with pharmaceutical and biotech companies, which pay for access to its models or for designed molecules, often with upfront fees, milestone payments and royalties if drugs succeed. It may also develop some drug programmes of its own. Releasing Chai-1 openly helped build its reputation among researchers, while more advanced models can support commercial partnerships.

Competition

Chai competes with Isomorphic Labs, the Alphabet company built on AlphaFold; Xaira Therapeutics; Generate Biomedicines; Absci; academic groups such as the Baker lab at the University of Washington; and pharmaceutical companies' internal AI teams. Open models such as Boltz also compete for researchers' attention. Chai's strengths are its team, its open research reputation and its focus on antibody design.

Chai Discovery at a glance

Chai Discovery is a San Francisco AI company founded in 2024 by researchers including Joshua Meier and Jack Dent, backed early by OpenAI. It builds models that predict molecular structures and design antibodies. It raised a US$130 million Series B co-led by Oak HC/FT and General Catalyst at a US$1.3 billion valuation in December 2025.

Timeline

• 2024: founded; raises seed funding including OpenAI; releases Chai-1.
• 2025: raises a Series A; releases Chai-2 for antibody design.
• December 2025: raises US$130 million at US$1.3 billion.

Why structure prediction matters

Most drugs work by binding to specific proteins in the body, changing how those proteins behave. To design a drug, scientists need to understand the protein's 3D shape and how a candidate molecule fits into it. For decades, determining structures required laborious laboratory techniques such as X-ray crystallography. AlphaFold, released by Google DeepMind, showed that AI could predict protein structures with remarkable accuracy, earning its creators a share of the 2024 Nobel Prize in Chemistry. Newer models, including Chai-1, extend this to complexes of multiple molecules, which is closer to what drug designers need.

Designing antibodies with AI

Antibodies are proteins the immune system uses to recognise threats, and antibody drugs are among the best-selling medicines in the world. Traditionally, antibody discovery involves immunising animals or screening huge libraries to find binders, then optimising them over many months. AI design aims to generate antibodies that bind a chosen target directly on a computer, reducing the number of laboratory experiments needed. Chai reported that Chai-2 produced binding antibodies for a high share of targets tested in initial experiments. If such results hold up across many targets, AI could dramatically shorten early discovery.

From design to medicine

Designing a molecule that binds a target is only the first step. A drug must also be safe, stable, manufacturable and effective in patients, and clinical trials take years and usually account for most of a drug's development cost. AI can speed early discovery, but it cannot yet remove the need for extensive testing. Investors in AI drug discovery are therefore betting on faster, cheaper discovery and higher success rates rather than instant medicines.

Who funds Chai Discovery

Chai's investors include Oak HC/FT and General Catalyst, which co-led its Series B, along with OpenAI, Thrive Capital and Menlo Ventures.

Valuation context

A US$1.3 billion valuation less than two years after founding reflects strong investor appetite for AI drug discovery and the scarcity of top AI and biology talent. Our guides to AI drug discovery companies and healthcare AI startups compare Chai with peers.

Risks

• Clinical uncertainty: designed molecules must still succeed in lengthy trials.
• Competition: many well-funded companies and open models target the same problem.
• Commercial model: partnership revenue can be lumpy and milestone-dependent.
• Validation: early laboratory results must be replicated across many targets.

Questions for pharma partners

• Which target classes have the models been validated on?
• How does intellectual property ownership work for designed molecules?
• What laboratory validation does Chai provide?
• How are fees, milestones and royalties structured?

A typical partnership

A pharmaceutical company may have a disease target, such as a receptor on cancer cells, for which conventional antibody discovery has struggled. It could partner with Chai, sharing information about the target. Chai's models would generate candidate antibodies predicted to bind it, which would then be tested in a laboratory. The best candidates would be optimised for properties such as stability and manufacturability. The partner would then take the leading candidate into preclinical studies and, if successful, clinical trials, paying Chai milestones as the programme advances. Such deals let pharmaceutical companies tap new technology without building it themselves, while giving AI companies revenue and real-world validation.

What to watch

Watch for pharmaceutical partnerships, new model releases, laboratory validation data and Chai's first drug candidates.

Takeaways

Drug developers can use AI to design molecules faster. Founders can see how open research can build credibility. Investors should remember that discovery speed does not remove clinical risk.

Sources

Every figure on this page comes from the public sources below. Where sources disagreed, we used the company's own announcement or a primary news report. Private-company data changes; check the date beside each figure.

1. Fierce Biotech