AIs combination of pattern recognition, enormous computational power, and deep customization enables a profound impact on biotechnology and pharmaceuticals. The opportunities are enormous, but the risks may be even greater.
This blog post is the seventh in the series on AI’s disruption of innovation
This blog entry is the seventh in a series about the disruptive impact of a technology based on a new form of intelligence that is self-learning, universally enabling, and allows for deep customization:
- Where does AI impact today?
- What is new and disruptive about AI?
- AI’s effect on robotics and experimental automation
- AI’s effect on climate and meteorological research
- AIs impact on social sciences
- AIs impact on education and mental health
- AI’s impact on biotechnology and pharmaceuticals
- AI’s effect on materials research and quantum chemistry
- AI’s effect on theoretical physics and mathematics
- What should investment strategies in AI take into account?
Biotechnology and pharmaceuticals normally develop over long timeframes
Natural biological evolution is the result of small, isolated DNA mutations that happened to provide advantages in specific contexts. As a result, biological evolution is extremely slow, random, imperfect, and associated with enormous inefficiencies.
By contrast, humans have always sought to optimize and extend life.
- The global market for disease treatment is over USD 4.3 trillion, with annual growth above 4%.
- The global market for pharmaceuticals is over USD 1.7 trillion, with annual growth above 6%.
However, pharmaceutical research is typically highly capital-intensive and time-consuming. Moreover, many drugs are more palliative than truly curative.
AI will at first significantly accelerate development speed
AI technology can primarily streamline and shorten development processes , including by optimizing regulatory documentation.
But more importantly, AI can open the door to individually tailored and curative treatments with far fewer side effects. Many conditions today are considered highly individual, whether due to unique genetic makeups or, especially, personal external factors (environmental exposure, nutrition, lifestyle, etc.).
This makes the market potential for customization very large. The more narrowly targeted the treatment, the greater the effect and the fewer the side effects. This is precisely where AI technology excels.
Development of “traditional drugs” will increase, partly due to “in silico” processes …
“Traditional” (broad-spectrum) drugs are expected to see moderate leaps in innovation. AI has demonstrated remarkable abilities in scanning and analyzing chemical libraries containing millions of compounds. This allows for predicting molecular affinity, toxicity, and pharmacokinetics before investments are made in costly lab trials. Similarly, AI can be used to design entirely new molecular structures tailored to precise biological targets. Big Pharma generally has a strong foothold in this part of the market.
In 2024, the company Insilico Medicine launched an AI-designed drug that slows the progression of idiopathic pulmonary fibrosis. Its development time was just 18 months, compared to a normal 4–6 years. Exscientia is another company in the sector attempting similar approaches to drug development.
AI particularly enables “in silico” development, where patient pathways and treatment effects are simulated virtually before expensive clinical trials are initiated.
… but especially epigenetics can accelerate development speed …
Epigenetics was conceptually understood in the 1970s and applied commercially from the 1990s. It concerns the chemical modifications that affect gene activity—without altering the DNA code itself. In other words, it is about how the gene is read, “switched on or off,” rather than the sequence of A, T, C, or G. While genetics is the hardware, epigenetics is the software.
Certain epigenetic patterns change with age, which is why research is ongoing into life-extending epigenetic medications.
Epigenetic changes also frequently occur in many types of cancer, even without DNA mutations. Some oncological treatments therefore attempt to “reset” epigenetics.
… as a result of pattern recognition across vast datasets
AI relies on massive computational power, pattern recognition through cross-referencing huge datasets, and self-learning capabilities. This makes it particularly well suited for identifying decisive biomarkers.
- The field is expected to achieve major breakthroughs in the coming years, as knowledge from over 214 million 3D protein structures and foldings is integrated. This could allow for predicting major pathogens and, consequently, for developing antiserums or vaccines.
The area is largely dominated by Big Pharma but could present significant VC opportunities at TRL 1–4 levels.
Epigenetic mechanisms may also prove to be tools in what many expect to be the truly transformative field in the years ahead: synthetic biology.
Synthetic biology may hold the potential for the greatest breakthroughs …
Synthetic biotechnology refers to the attempt to design or redesign biological systems. The field is about creating biological organisms, systems, or components through the use of synthetic DNA (e.g., CRISPR). Here, AI’s defining characteristics are ideal for leaps in innovative power, and Big Pharma is highly interested, though not yet deeply established.
- Currently, the sector is a thriving network of startups. Major universities, often in collaboration with Big Pharma, act as development dynamos, in the US, UK, France, EU, Canada, Brazil, Africa, and beyond.
… with impacts on climate, environment, health, hazards, and agricultural products
Key near-term opportunities include:
- Climate: breakthroughs from the design of CO₂-absorbing microorganisms (artificial photosynthesis).
- Environment: engineered organisms for biodegrading plastics and toxins; even more ambitious projects include recreating or creating ecosystems (e.g., the Mammoth Project).
- Medicine and health: applications such as gene therapy, microorganisms used as drugs (insulin, antibiotics, vaccines, etc.), personalized medicine based on synthetic DNA, or cellular biosensors. The field opens the door to curative therapies for hereditary diseases (e.g., Alzheimer’s, Tay-Sachs), life extension, and pandemic prevention (e.g., engineered genetic immunity).
- Practical uses: development of clothes or creams able to detect pathogens and toxins, useful for pollution monitoring or epidemic zones.
- Agriculture: synthetic meat and dairy, drought-resistant plants, biofabricated fertilizers, and reduced antibiotic use.
Theoretical perspectives reach even further …
In the long run, George Church of Harvard’s Wyss Institute has proposed several core ideas:
- Redesigning life from the ground up by synthesizing new genomes or even creating entirely new forms of life (e.g., “mirror life”). Recoding entire bacterial genomes could yield virus-resistant organisms, a major step toward biosafety. Programmable organisms could be farmed to produce materials, fuels, and pharmaceuticals.
- De-extinction: restoring extinct species like the mammoth, potentially recreating ecosystems and testing synthetic biology at scale.
In addition, the area could open for DNA-based data storage, i.e. encoding data in DNA-like sequences. This offers vastly higher density and durability (security) than digital media.
… and the timeline for these may be relatively short
The field has advanced rapidly. Companies such as IDTDNA already offer full-synthetic DNA printing via web forms—users simply add water to activate it.
Synthetic biology could also enable new business models in healthcare. Medium-sized labs may, in time, take over oncology treatments from hospitals, from diagnosis through stem cell therapies to monitoring. DNA decoding could also allow simulation of an individual’s response to a drug, enabling precision medicine.
Geopolitically and in terms of ESG, synthetic biology could increase strategic autonomy for many countries (local food production, wastewater treatment, etc.). Conversely, it may also deepen inequality (technology divides, food control).
The societal risks, however, are immense, and practically impossible to control
The perspectives of synthetic biology are enormous, and so are the risks. George Church has advocated for genetic kill switches and biological containment (organisms surviving only under specific conditions). Yet this is akin to Pandora’s box. For comparison: developing an atomic bomb required massive coordinated effort. In contrast, the danger with synthetic biology is that ever fewer people can create pandemic-scale bioweapons, in ever less time, at ever lower cost.
- Already today, it is possible to modify simple bacteria or viruses for under $100. But modified organisms can replicate and mutate into something unforeseeable.
- In the short term, the risk of basic CRISPR experimentation on humans remains minimal, too expensive and dependent on critical technologies.
Still, NVIDIA, Google, and Microsoft are betting heavily on this field, with Amazon and even Salesforce seeking to carve out positions in parts of the market.