In the past, I briefly discussed the use of artificial intelligence (AI) and machine learning (ML) in drug discovery. Especially when it comes to the potential of rapidly testing new compounds to treat hair loss. Funding for drug discovery startups that use AI is now really taking off. Heavyweight companies such as Amazon, Anthropic, Google and Palantir increasingly focusing in this area. However, the AI drug boom is still in its early stages.
Note that this post was originally written in November 2022, but needed an update due to a many new developments.
Hair Loss Companies using AI to Develop new Drugs
A number of hair loss companies are currently developing new drugs via the use of AI. I will update this section regularly.
The most widely covered of these is Absci (US), an artificial intelligence drug and biologic creation company that is developing novel treatments via the use generative AI. Their most anticipated product is a prolactin receptor (PRLR) targeting injection based hair growth treatment called ABS-201. It is in Phase 1 clinical trials as of 2026.
In 2026, a new start-up named RE:YOU claimed to have developed four new molecules to benefit hair growth using AI.
A few years ago, there was much hype about a new AI drug discovery company named Insilico Medicine (Hong Kong). Among the conditions the company aimed to develop drugs for included hair loss. While they mentioned hair in a number of their past press releases, you no longer see it on their pipeline page. Update: Eli Lilly (US) and Insilico struck a $2.75 billion AI drug discovery deal in March 2026. And Takeda (Japan) and Insilico struck a $600 million AI drug discovery deal in July 2026.
Another hair loss company making use of AI is South Korea’s Epibiotech. In October 2021, it signed an agreement with CN.AI in order to accelerate the discovery of new hair loss drug candidates.
Also of interest, in December 2019, Iktos (France) and Almirall (Spain) signed an agreement in which Iktos’ AI modelling technology would be used to design novel optimized compounds for Almirall. The latter is a company that is entirely focused on skin and other dermatological conditions. They currently make the world’s only topical finasteride product that has undergone rigorous clinical trials.
Using AI to Predict Hair Loss Compounds
What made me first write this post was an interesting article titled: “Researchers use AI to predict compounds that could neutralize baldness.” The actual study from China is here and it was published on October 20, 2022.
In the article, they mention that male pattern hair loss is caused by androgens, inflammation or an overabundance of reactive oxygen species. One potential treatment for the last mentioned is via the creation and utilization of “nanozymes” that mimic the superoxide dismutase (SOD) enzyme. SOD helps fight damaging oxygen free radicals.
The scientists tested machine-learning models with 91 different transition-metal, phosphate and sulfate combinations. A highly efficient manganese thiophosphite (MnPS3) based SOD mimic was discovered using machine learning tools. These ML techniques predicted what cobination would have the most powerful SOD-like ability.
The team subsequently prepared MnPS3 microneedle patches which they used to treat androgenic alopecia-affected mouse models. Microneedling allowed the MnPS3 to penetrate deep layers of the skin (where hair follicle stem cells reside) and remove the excess reactive oxygen species. Within 13 days, the animals regenerated thicker hair strands that more densely covered their previously bald backsides.
Also check out how it is possible to speed up drug discovery with diffusion generative models (such as the DiffDock molecular docking model).
Opensource Databases and Resources
Although not exactly AI, I find the increasing number of online opensource resources, databases, repositories and collaborative research tools encouraging.
Make sure to read my post on the publicly available DeepMind AlphaFold protein database. You can search for “hair” in there and get 2,200 results as of today.
In addition to the general DeepMind/AlphaFold GitHub, we also have hair specific ones such as the BiernaskieLab GitHub.
Also of interest are open source sites such as the Driskskell Lab’s skin regeneration and wound healing related datasets.
More recently, Dr. Maksim Plikus and his team at UCI developed CellChat, which enables the better understanding of cell-to-cell communication and signaling.
Other AI Applications in the Hair Loss World
Note that artificial intelligence is also being used for other purposes beside drug discovery in the hair loss world. Among these include:
New AI Solution to Diagnose Hair Loss Type via Scalp Biomarkers
Cosmetics manufacturer Kolmar Korea (South Korea) has developed an artificial intelligence-based solution that can diagnose hair loss using scalp biomarkers. The technology can diagnose 16 different types of androgenic hair loss (nine male and seven female). A dermatologist collects samples of a patient’s scalp, places them on proprietary analytics equipment, and has the AI-powered tool screen scalp surface biomarkers.
Kolmar Korea expects that its diagnostic tool will help hair loss patients and dermatologists choose optimized treatments. The company also plans to develop various cosmetics that target each of the 16 different types of androgen related hair loss.
