Top 5 AI- ja digitaalisen bioteknologian yritystä (kesäkuu 2026)

AI-revoluutio biotekniikassa

No sector is left unchanged by the power of AI and digital modeling, and Biotech is no exception.

Tämä johtuu siitä, että biologia on kauaskantoisesti kaikkein “sekavampi” luonnontieteistä. Kemia tai fysiikka voi käsitellä hyvin hallittuja ympäristöjä, puhtaita yhdisteitä jne. Biologian on käsiteltävä ennalta olemassa olevia äärimmäisen monimutkaisia ja jatkuvasti muuttuvia järjestelmiä. Lisäksi, kun analysoidaan vain yhtä proteiinia, biokemistit tarkastelevat tuhansia tai miljoonia atomeja. Näin ollen kaikkien mahdollisten kemiallisten reaktioiden ennustaminen voi olla todella vaikeaa.

Suuret tiedot, tekoälymallit ja digitalisaatio luovat edellytykset tietämyksen vallankumoukselle biotekniikan tutkimuksessa.

Biolääketieteen ensimmäinen aikakausi oli ampua pimeässä ja katsoa, mikä toimi.

Olemme nyt vankasti vakiintuneet genomiikan aikakauteen, jossa voimme keskittyä tiettyihin kohteisiin, kuten yhteen viallisena olevaan geeniin.

With the incoming digital revolution, we can replicate complete proteins, cells, or even entire organs and bodies in a virtual environment.

Mitä se muuttaa?

A big part of why genomics and precision therapies are taking over “traditional” chemical drugs has been a very poor success rate for new drugs in the last decade.

Ehkä voidaan tuottaa FDA:n hyväksymä lääke kymmenelle tuhannelle ehdokkaalle. Jokainen vaihe on testattava laboratoriossa, elävissä soluissa, eläimissä tai ihmisissä.

Tämä edustaa usein yhden kahden vuosikymmenen menetettyä aikaa, sekä monia, monia miljardeja dollareita hukkaan.

Lähde: Biosourcing

Sokeasti ampuminen ei ole enää toimiva suunnitelma lääkekehityksessä. Tästä syystä tutkijoiden tarvitsee digitaalisia ennustemalleja, jotka voivat ennustaa ennen fyysisiä testejä, onko lääke hyvä ehdokas.

Joten ei ole yllättävää, että lääkekehitys on useimpien AI-biotekniikkayritysten liiketoimintamallin eturintamassa.

Uudet menetelmät, kuten koneoppiminen, antavat ohjelmistolle mahdollisuuden “arvata” todennäköisimmän vastauksen todennäköisyysmenetelmällä sen sijaan, että se olisi täysin “mekaaninen”/algoritminen.

Kuten suurin osa koneoppimisteknologiasta, paljon työtä tehtiin koko vuosikymmenen ajan, ja vain alan asiantuntijat kiinnittivät siihen todellista huomiota.

 vuonna 2020, kun Alphabet/Google DeepMind ratkaisi 50‑vuotisen haasteen proteiinien taittumisessa. Ohjelma on sen jälkeen mallintanut suurimman osan kaikista elävien organismien tunnetuista proteiineista, ja Google luo uutta yritystä, Isomorphic Laboratories, auttamaan uusien lääkkeiden tunnistamisessa.

Top 5 AI- ja digitaalisen bioteknologian yritystä

For investors, Google might be a great play on AI in general, but the biotech aspect will be a tiny segment in a very large company. So, this article will review publicly listed companies that are solely dedicated to the topic of AI and Virtual Biology.

For the same reason, we will not look at companies involved in AI hardware, like Nvidia and its genomics library Parabricks.

(yritykset on järjestetty markkina-arvon mukaan artikkelin kirjoitushetkellä)

1. Roivant Sciences Ltd.

(ROIV )

The company specializes in acquiring biotech startups and boosting their chances of achieving commercialization through subsidiaries called -vant (as each will have “vant” as the last part of their name).

Part of these acquisitions was Silicon Therapeuticsin ostaminen $450M. Thanks to a supercomputer and custom computing hardware, Silicon Therapeutics is developing new molecules. This added to a preexisting AI biotech stack portfolio, VantAI.

Roivant also owned the “vant” Datavant, a big data solution for healthcare, selling to hospital pharmaceutical companies, insurance, etc… with regulation-compliant and privacy-respecting procedures.

Other “vant’s” are also data or digital simulation-oriented, like the “Accurate All-Atom Physics-Based Simulations” of Psivant. Or the clinical trial intelligence software/platform Lokavant.

Lähde: Roivant

Still, most of the company’s income derives from pharmaceutical sales of approved products.

Overall, Roivant can be a way to play the data side of biotech, not only digital biology but also medical records, clinical trials, etc….; while at the same time touching on other innovative medicine, especially for skin care, with Vtama for psoriasis.

2. Schrödinger, Inc.

(SDGR )

The company specializes in physics-based models to find the best possible molecule for a given goal, balancing out conflicting metrics like potency, solubility, half-life, synthesizability, etc…

It also uses machine learning, but the addition of a physics-based model allows it to be tested in entirely novel fields for which no data set exists to “train” the AI. This allows Schrödinger to go from 1 billion potential molecules to just 8 solid candidates in a matter of days, exclusively through digital calculation.

Lähde: Schrodinger

Schrödinger solmi Bayerin kanssa 5‑vuotisen yhteistyösopimuksen vuonna 2020, jonka liikevaihto on $10M. Sopimuksen idea on käyttää Schrödinger‑teknologiaa yhdessä Bayerin in‑silico‑ennustemallien kanssa.

Another recent partnership is with Lilly, with up to $425M in total milestone payments for successful discovery.

Past collaborations included Takeda, Sanofi Bristol Myers Squibb, and other smaller pharmaceutical companies.

Lähde: Schrodinger

Overall, Schrödinger is building a growing portfolio, including more and more proprietary and fully-owned molecules. While not pre-revenue, the company is still not profitable, focusing on expansion and R&D spending to improve its technology.

The company is also looking at expanding toward new segments beyond drug discovery, like complex biopharmaceuticals or even materials like chemicals, batteries, or polymers.

Lähde: Schrodinger

Investors will want to keep an eye on the new collaborations, as they will reflect the advances of Schrödinger’s technology, as assessed by the leaders in the industry, as well as possible success in expanding the core technology to new markets.

3. Exscientia

(EXAI )

The company is using AI to develop precision therapies. It runs a “full stack” AI drug discovery technology with dedicated software at every stage of the drug discovery process.

Lähde: Exscientia

Exscientian teknologia lyhentää 70 % ajasta, joka tarvitaan biologisesta kohteesta vastaavan lääkkeen löytämiseen, ja vähentää pääomakustannuksia 80 %.

Tämä johti neljään yhdisteeseen varhaisessa kliinisessä vaiheessa, yhteensä 30 ohjelmaan ja $6,5 Mrd tuloihin kumppaneiden välisistä virstanpylväistä. Pääasiallinen fokus on onkologia (syöpä) ja tulehdussairaudet.

Lähde: Exscientia

The company is in a very comfortable financial position for an early-stage drug discovery company, with $625M in Q3 2022, for a net cash burn of just $15M.

This might be an interesting option for investors looking at a well-established AI drug discovery company with a very large cash runway and multiple ongoing partnerships for extra safety.

4. Absci Corporation

(ABSI )

The company was founded in 2011, with locations in Vancouver, New York, and Zug, Switzerland. It has added to its initial technology the IP of 2 AI-biology acquisitions in 2021, Totient (antibodies) and Denovium (cell lines).

The company is mostly focused on antibody design, creating new from scratch antibodies (“de novo antibodies”), and testing them in laboratories in a 6-week process.

They were the first, in maaliskuu 2023, to be able to design a functional antibody without any pre-existing data, a method also called “zero-shot.”

Absci has established collaboration with Merck (total of $610M in upfront fees and future milestone potential payment) and Astellas for new product discovery, as well as a partnership with Nvidia to improve the hardware architecture behind Absci technology.

Absci is still at an early stage but has shown tremendous potential and innovation potential already. Investors in the company will need to be on board with the “nothing is impossible” ethos of the company and its brilliant founder and hope for the recent collaboration agreements to be the first in a long series.

5. e-therapeutics plc

e-therapeutics is focused on developing in-silico new RNAi (RNA interference) therapies. It hopes that combining emerging technology, RNAi, and computational drug discovery will give it a significant edge over its competitors.

It is also monetizing the discovery on its platform with other pharmaceutical companies, of which the largest is the large blue chip Novo Nordisk.

The company is at a very early pre-revenue stage and had to raise £13.5 million in the summer of 2022. The company registered a net loss of £2.8 million in H1 2022m for a cash balance at the time of £21.8 million.

Investors in e-therapeutics will need to keep an eye on the cash available and hope for new discoveries and partnerships’ revenues to turn the company profitable ultimately.

Digitaalisen biologiaportfolion rakentaminen

This is a difficult sector to invest in, as it combines 2 very complex technologies: AI + advanced biotechnology. This makes it pretty much a “black box” for investors, even if they have some expertise in one of the 2 fields.

In addition, most of the companies in the sectors are focused on the same markets, mostly small molecule discoveries and antibody designs, with maybe cell lines as well.

So diversification would make for a safer investing strategy because very few people will be able to be sure to have picked the “winner.” In addition, the market is expected to grow very quickly, at a CAGR of 45% between 2022 and 2027.

So, widespread exposure is more likely to catch this growth without overly relying on specific mathematical models or methods in a very quickly changing and competitive arena.