Artificial intelligence (AI) is revolutionizing biotechnology, enabling groundbreaking innovations across a wide range of applications. Recent developments, such as the $80 million funding round secured by Israeli AI-biotech company CytoReason, led by industry giants Nvidia and Pfizer, highlight the growing confidence in AI’s transformative potential. This investment reflects projections that the global AI in the biotechnology market will expand from US$ 5.4 billion in 2024 to US$ 26.3 billion within the next decade.
According to Start-Up Nation Central (SNC), AI represents about 30% of Israel’s start-ups and attracts nearly half of total company funding. As a result, the number of active AI companies in Israel has tripled over the past decade.
From predicting molecular interactions and designing new proteins to analyzing complex datasets for diagnostics and synthetic biology, AI is transforming how biotech solutions are discovered and developed.
AI has transformed traditional biotechnology by analyzing vast datasets, optimizing processes, and uncovering solutions that were previously unattainable. From small molecules and biologics to diagnostics and synthetic biology, AI-driven platforms are accelerating innovation and cutting years off development timelines.
For molecular and therapeutic applications, tools such as AtomNet and Exscientia predict interactions between compounds and biological targets, streamlining the discovery of new treatments.
In protein engineering and biologics, tools like DeepMind’s AlphaFold enable highly accurate protein structure prediction, advancing not only biologics development but also basic research, industrial applications, and sustainability efforts. Similarly, platforms like Cradle leverage AI to design new proteins for therapeutic, industrial, and environmental uses.
AI is revolutionizing diagnostics, as seen in platforms such as Tempus, which utilize AI to analyze genomic and clinical data for personalized cancer treatment. In synthetic biology, companies like Arzeda leverage AI to design and optimize enzymes for industrial applications, including sustainable manufacturing and bio-based materials.
These advancements demonstrate AI’s ability to unlock new possibilities across the biotechnology spectrum.
Creating opportunities using AI-driven biotech
AI-driven biotech inventions create unprecedented opportunities but also introduce significant IP challenges. Patentability requires novelty, inventive step, and industrial applicability. However, the use of AI to generate solutions can blur the lines of inventive step, as the contribution of human ingenuity may be less apparent. This raises critical questions about inventorship, as patent law traditionally requires a human inventor. Entrepreneurs must carefully document how human input guided the AI, clearly demonstrating its essential role in achieving the inventive step and meeting legal requirements for inventorship.
The importance of addressing these challenges is underscored by the growing role of AI in patent activity. Last year, the European Patent Office (EPO) reported approximately 15,000 patent filings in computer technology including AI advancements, making it the third-largest field.
Disclosure adds another layer of complexity. Patent applications must provide enough detail to allow replication by others skilled in the art, which often involves explaining how the AI algorithm contributed to the invention. However, disclosing too much risks exposing proprietary technologies, such as AI models or datasets, to competitors. Balancing transparency with the need to protect sensitive information is crucial.
To address these challenges, strategic planning is essential. Filing patent applications early, even at the provisional stage, helps secure priority dates and mitigate risks from public disclosures. Innovators should emphasize the unique features of their discoveries and provide robust scientific evidence to support their claims.
Trade secrets can offer an effective alternative, particularly for AI algorithms and datasets that are central to the innovation but may not be suitable for patent protection.
By enforcing strict confidentiality agreements and implementing robust security measures, innovators can protect these assets while retaining control. Combining patent protection with trade secret strategies creates a comprehensive framework to safeguard discoveries and stay competitive in the rapidly evolving landscape of AI-driven biotechnology.
The writer is a patent attorney at Reinhold Cohn Group.