With AI, it's possible to develop a drug twice as fast. Which companies is Cathie Wood betting on?

The pharmaceutical industry has long been using AI to develop drugs. According to estimates by the analytics firm Norstella, conducted as early as 2024, four out of five companies used algorithms in at least one program for developing new drugs.

The logic is clear: according to estimates by the Boston Consulting Group, artificial intelligence has the potential to reduce the initial stages of development testing by 30–50% and cut costs by 25–50%. These are significant savings, considering that, on average, it takes 10–15 years and costs $1–2 billion to bring a single drug to market.

The latest industry statistics show that drugs developed using algorithms are successful in 80–90% of cases—a rate twice as high as that of drugs developed “manually,” according to the 2 Minute Medicine website.

But—and here's the catch—only in the first phase of clinical trials.

Further on, the statistics are different: in the second, intermediate phase, approximately 40% of drug candidates are successful, which is comparable to the rates for “conventional” molecules. By mid-2026, only eight drug candidates had completed this phase. Among them is rentosertib for the treatment of idiopathic pulmonary fibrosis from Hong Kong-based Insilico Medicine (it began its final phase of trials in July). We covered Insilico Medicine’s drug development programs and history in detail in our newsletter focused on small- and micro-cap companies. You can subscribe to the English-language newsletter here.

Another drug is the antibody GB-0895 for the treatment of severe asthma from the biotech company Generate:Biomedicines—the U.S. Food and Drug Administration (FDA) authorized it to skip directly from Phase 1 to Phase 3, and it began trials in December 2025.

The only company in the world to have successfully completed all phases of clinical trials for a drug developed using algorithms is the Japanese company Takeda. Its drug, zazocitinib, is intended for the treatment of psoriasis. The drug was developed by Nimbus Therapeutics in collaboration with Schrödinger, using computer modeling to identify a molecule that blocks the TYK2 enzyme, which plays a role in inflammation. Takeda acquired the drug in 2023 and has continued clinical trials. The FDA may approve the drug as early as 2027.

This sector attracts many players—both industry insiders and outsiders. In 2023, chipmaker Nvidia announced plans to expand its investments in this area, and in 2025, its competitor, AMD, did the same.

Cathie Wood and her investment firm, ARK Invest, are considered among the largest and most influential investors in the application of artificial intelligence to biotechnology and drug development. Since the beginning of the year, her exchange-traded fund, ARK Genomic Revolution, has posted a return of 70%. By comparison, the S&P 500 Index, a broad-market benchmark, rose 13.4% over the same period.

Three companies developing drugs using artificial intelligence in which ARK Genomic Revolution invests:

Recursion, with a market capitalization of $2.3 billion on the Nasdaq, uses AI models to develop new drugs—not through conventional computer modeling, but through the large-scale automation of real biological experiments.

Using its proprietary Recursion OS technology platform, the company conducts up to 2 million experiments per week. This has enabled it to collect more than 50 petabytes of data—roughly equivalent to 10 billion songs, which would take approximately 66,600 years to listen to.

Recursion uses this database to identify promising molecules—both for its own portfolio and in collaboration with other companies, including pharmaceutical giants Roche and Bayer. In addition, it sells access to its accumulated data to scientists.

The company's innovations are attracting more than just industry players. In 2023, Recursion raised $50 million from chipmaker Nvidia, which stated that its goal was to accelerate drug development using AI. Together, they built one of the world’s most powerful supercomputers, which runs on Nvidia graphics processing units.

The small-cap biotech portfolio currently includes five drug candidates at various stages of clinical trials: four for cancer and one for the treatment of familial adenomatous polyposis, an inherited disorder in which hundreds of polyps form in the colon and rectum. Two other drug candidates have not yet reached the stage of human testing.

For now, Recursion generates revenue primarily through payments from partner companies. At the end of the second quarter, this figure fell by nearly 60% year-over-year, to $7.7 million. The net loss for the same period decreased by 24% to $131 million.

Wall Street, on the whole, takes a cautious view of the company’s prospects: four analysts recommend holding its stock, and three recommend buying it. The average price target is $7.22, which implies upside potential of nearly 83% relative to the closing price on October 8.

What sets Absci—which has a market capitalization of $1.58 billion—apart is that it uses neural networks to search not only for compounds that can treat diseases, but also for targets—that is, the cells those compounds will attack.

The typical process works like this. First, scientists select a target by “manually” reviewing the literature and databases. Then they formulate a hypothesis that answers the question of why this particular target must be targeted to treat the disease. In the next stage, they propose a solution—that is, a new drug. “It sounds simple, but in reality, it’s one of the most complex undertakings ever attempted by humankind,” Absci writes on its website.

The reason is that, to date, there is no comprehensive information on the onset and progression of diseases: most of our knowledge about molecules in the human body is based on indirect data obtained, for example, from tests on animals or individual cells. As a result, most new drugs fail in clinical trials.

The extremely low success rate—only about 12% of drugs make it to the market—suggests that the “wrong” target was selected during the target identification process, Absci explains.

She approaches the process in exactly the opposite way: first, she uses AI to study human cells that fight disease, and then she determines exactly which disease they are fighting. That’s how she selects a target. After that, Absci recreates the antibodies in the lab—and this becomes the starting point for drug development. This is exactly how the company obtained the ABS-201 antibody for the treatment of androgenetic alopecia—a hereditary condition in which hair loss occurs under the influence of male sex hormones. It is currently in the early stages of clinical trials.

The company plans to test this same antibody on women with endometriosis. Its partner in this effort is pharmaceutical giant Eli Lilly, which has invested $40 million in the company. The agreement, however, does not grant the American pharmaceutical company any development rights, the small-cap company emphasizes.

Absci's other partners include the pharmaceutical giant Merck, the Spanish pharmaceutical company Almirall, chipmaker AMD, and the Bill & Melinda Gates Foundation. The Gates Foundation has awarded a grant to a joint project between the biotech company and the California Institute of Technology (Caltech) to develop an HIV vaccine.

At the end of the second quarter, the company reported revenue of $300,000—half as much as in the same period of 2025. Meanwhile, the net loss increased by 8%, to $33.2 million.

Wall Street is generally optimistic about Absci's prospects: ten analysts recommend buying its stock, and only one recommends holding it. The average price target is $14.4, which is about 60% higher than the stock's closing price on October 8.

Schrödinger, with a market capitalization of $2 billion, is quite different from the other two companies in Wood's portfolio.

It was founded in 1990 and, for decades, focused exclusively on developing software for molecular design, licensing it to scientists and pharmaceutical companies. This eliminates the need for them to mix substances in a test tube, calculate synthesis possibilities, and select the potentially best compounds, according to a description provided on a blog by pharmaceutical giant Bayer, which has been “using Schrödinger’s technology for decades.”

Bayer is not the only company using the software developed by the company. Two anticancer drugs have already been created using it: Tibsovo from Agios Pharmaceuticals and Idhifa, a joint development by Agios and Celgene.

Schrödinger didn’t venture into the pharmaceutical industry on its own until 2009, when it founded Nimbus Therapeutics in partnership with Atlas Ventures. The company develops drugs using AI, advances them to the early stages of clinical trials, and then sells them to pharmaceutical giants. For example, in 2016, Gilead Sciences acquired its program for developing drugs to treat liver diseases. In 2023, Japanese company Takeda acquired a drug with the potential to treat psoriatic arthritis and systemic lupus erythematosus for a $4 billion upfront payment and $2 billion in potential future payments. This drug could become the first approved therapy developed using AI.

Nearly a decade later, in 2018, Schrödinger began developing its own drugs. The company’s portfolio currently includes two experimental oncology drugs in early-stage clinical trials. Five more, in various therapeutic areas, have not yet reached human trials. In addition, Schrödinger is participating in 19 programs with other pharmaceutical companies and is entitled to royalties from their future sales.

At the end of the second quarter, the company’s revenue rose 7.5% year-over-year to $58.89 million. Net income totaled nearly $6 million, compared with a loss of $43.2 million a year earlier, primarily due to the completion of Eli Lilly’s acquisition of Ajax Therapeutics. However, the operating loss amounted to $41.5 million.

Five Wall Street analysts recommend buying the company's stock, while two recommend holding it. The average price target is $21.43—nearly 20% below the closing price on October 8.

This is not intended as individual investment advice.