Biotech companies are finding generative AI has applications in drug discovery, protein engineering, and personalized medicine.
A recent research report has outlined just how popular generative AI has become with biotech companies. Its use in the market is expected to reach a value of around $472 million by 2032, with a compound annual growth rate (CAGR) of 24.9% during the forecast period. Interest in the technology is broad with many application areas being explored.
Why biotech is embracing generative AI
Generative AI is gaining significant attention thanks to its ability to simulate and generate novel molecules, proteins, and genetic sequences. This ability has applications in drug discovery, protein engineering, and personalized medicine. These algorithms create virtual libraries of comments that researchers can quickly screen to accelerate the drug discovery pipeline. They can also explore vast sequences and structures which could revolutionize fields like enzyme catalysis and antibody engineering.
An exciting possibility lies in the field of personalized medicine. Generative AI can analyze patient data and genetic information to produce tailored treatment plans and optimize therapies. They also help keep patients safer by minimizing adverse reactions by predicting drug responses for individual patients.
See also: Quantum Computing Acceleration of AI in Pharma on the Rise
North America is at the forefront
The study shows North America is at the forefront of the technology’s adoption, with Europe and Asia Pacific close behind. While Latin America, the Middle East, and Africa are also interested in these capabilities, challenges related to infrastructure and funding are slowing adoption.
The market continues to face challenges, including the ethical considerations of leveraging AI and patient data and regulatory approval and acceptance. For now, generative AI algorithms have limited generalizability within the models, something researchers must overcome for adoption to become more widespread.
To utilize generative AI’s full potential in the biotech market, researchers need to address these challenges head-on by collaborating with AI startups and integrating Omics data (data generated by the -omics: genomics, proteomics, etc.). With careful consideration of ethical implications, generative AI has the potential to impact the biotech industry significantly over the next decade.
