Patents for AI in Biotech and Synthetic Biology - Jonathan Cartu Internet, Mobile & Application Software Corporation
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Patents for AI in Biotech and Synthetic Biology

Patents for AI in Biotech and Synthetic Biology

Artificial Intelligence (AI) inventions have aided development in nearly every industry, but perhaps none more so than synthetic biology. For synthetic biology researchers, AI has developed into a vital tool to create cutting edge applications. Growth is expected to accelerate with the AI healthcare market set to reach $6.6 billion by 2021, a 40 percent growth from its current size. Biotech and synthetic biology companies that use AI and investors in these companies should be aware of various legal aspects related to patenting.  This blog is part 1 of a multi-part series that explores various patenting considerations for AI in biotech and synthetic biology. Key considerations for protecting AI inventions in biotech and synthetic biology include:

  • What is the best protection for the specific invention – traditional intellectual property (IP) protections or contractual, or both?

  • Is the invention eligible subject matter for patent protection under rapidly evolving case law?

  • Is the scope of protection altered for machine learning inventions when applied to different users or customers?

  • Who are the inventors when the invention is AI generated but human designed or curated? See here for a discussion of the recent notice from the U.S. Patent and Trademark Office (USPTO) on AI.

  • Are predictive algorithms eligible for a broader scope of IP protection based on the ability to predict outcomes? And can this be used as a competitive advantage to shut out other third parties in the marketplace?

  • Who is the owner of iterative data generations created by machine learning?

To set the stage for exploring the role of AI in biotech and synthetic biology advancements, it is helpful to consider some of the applications in the industry and potential issues in employing traditional IP protections, like patents. The following fields have seen developments on a meteoric pace using AI technology: sequencing and functional genomics; drug design, discovery, and testing; pharmacology; big data analytics; cancer diagnosis; and target identification and designing constructs, just to name a few. Each area of rapid development faces its own challenges for protection of the IP developed.


Companies and researchers are using AI to advance DNA sequencing. For example, Google’s DeepMind is developing a deep learning technology that can be used to reconstruct true genome sequences from high-throughput sequencing data. A team comprised of Microsoft researchers and collaborators from MIT, Harvard, UCLA, and Massachusetts General Hospital have developed a machine learning tool that improves the accuracy of CRISPR technology by predicting and preventing CRISPR from accidentally editing genomic regions similar to the target region.

AI is also being used to advance the field of functional genomics, a field of molecular biology that attempts to describe gene (and protein) functions. These developments are highly dependent upon the large data collections for genome sequencing. Researchers just published a study identifying a likely key cause of autoimmune disease as early activation of memory T cells. Researchers used a new computational method called CHEERS to enable them to identify cell states relevant for immune disease.

One major issue with patenting sequencing inventions is that, as of 2013, human genes are not patentable. However, human DNA sequences manipulated in a lab are patentable as they are not found in nature, allowing the patent owner to control the use of that genetic modification. 

Pharmaceutical Design, Discovery, and Testing

AI inventions are revolutionizing the drug design, discovery, and testing industry. AI-based drug designer Insilico Medicine has raised $37 million to help commercialize its technology, on the heels of a landmark paper for the company that showed its computer networks were able to generate, synthesize, and preclinically validate a series of promising compounds from scratch in less than 50 days. Typically, drug discovery starts by testing thousands of molecules over several years to eventually get down to a few novel molecules to take to human trials. Insilico Medicine’s AI technology, which combines molecular generation models with generative models, accelerated this process from years to weeks. This use of computer networks to design new drug compounds raises the question of who is the inventor—is it the inventor, the computer networks or the humans who designed the computer networks to generate, synthesize, and validate the new drug compounds? Insilico Medicine released the MOSES source code as open source, but aims to use this technology to create new therapeutic programs for cancer, immunology, fibrosis, nonalcoholic steatohepatitis and central nervous system conditions. Most open source programs are governed by a license that restricts the use of the source code or at least requires copyright acknowledgment, but the MOSES source code was released without even copyright ownership claims.

AI technologies are engendering the development of a host of new drugs based on a greater understanding of protein structure. Google’s DeepMind has led to an improvement in predicting the physical structure of proteins, the “basic building blocks of disease.” In turn, this has aided in the advancement of drug design. Knowing the shape of a protein allows researchers to know how a drug will bind to that protein and enable the development of new drugs. Codexis has developed proprietary program called CodeEvolver that uses machine learning algorithms to learn protein sequence changes and their impacts on protein function. MIT CSAIL scientists have also developed a system that they hope will aid in protein engineering. The machine learning model breaks down amino acid chains to determine a protein’s structure. By determining the protein’s structure, the team aims to design proteins and enzymes.

These developments raise the question of whether the greatly expanded knowledge of protein structure is eligible for traditional IP protection. If so, are the AI systems that were essential in creating the new drugs creditable inventors? The USPTO recently issued a call for comments on whether “patent laws and regulations regarding inventorship need to be revised to take…


Computer Network Development Jon Cartu

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