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Patenting Considerations for Artificial Intelligence in Biotech and Synthetic Biology

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: 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.

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.

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.

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.

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 into account inventions where an entity or entities other than a natural person contributed to the conception of an invention.” A process that employs machine learning to identify eligible protein structures through mathematical processes that could technically be done by a human over the course of many years, if not decades, may not even be eligible for patent protection.

High-throughput screening (HTS), a practice that has been in use for almost 30 years, leverages the advantages of robotics, data control software, and other devices to allow researchers to rapidly conduct millions of pharmacological tests, the results of which identify the starting points for drug design.

As discussed above, Google’s DeepMind system uses machine learning to predict the three-dimensional shape of proteins, which is integral in understanding how other molecules bind to the protein, an insight that is crucial to drug development.

At the Center, an AI predictive model was developed to determine if a particular treatment will be effective for a particular patient by finding genomic biomarkers.  There are two key reasons why obtaining a patent for diagnostic algorithms is very tough.

He labels this a “chicken and egg” scenario, explaining that “discovering a treatment for Alzheimer’s requires lots of clinical trials for new drugs—but it’s difficult to enroll participants without a way to identify people who have the disease early enough for potential treatments to work.” Finding a treatment for Alzheimer’s has the added complication of needing to develop drugs that can bypass the blood-brain barrier (BBB), a selective semipermeable structural and chemical boundary that surrounds the brain and typically prevents foreign objects from invading the brain tissue.

World Summit AI | Meet the world’s brightest AI brains | Oct 2019 | Amsterdam

As Global CTO for Sales, Patricia helps define mid and long term technology strategy, representing the needs of the broader Dell EMC ecosystem in strategic initiatives.

Patricia is the creator, author, narrator, and graphical influencer of the educational video series Dell EMC Big Ideas on emerging technologies and trends.

As CTO, Patricia was responsible for defining, and communicating the medium- to long-term vision EMC would embrace for delivering solutions to automate the management of Information Infrastructure resources.

In that capacity, Patricia was responsible for researching emerging technologies and for defining the strategy that Smarts would take in bringing solutions to market to address the challenges introduced by these technological advances.

Founded in 1955, the Columbia Engineering Board of Visitors is approved by the Columbia University Trustees to “advise and assist the Trustees, the Faculty of Engineering, and the Dean in the development of the school.” Patricia is the chairman of the advisory board for the Data Science Graduate Program at Worcester Polytechnic Institute where she will advise and assist in the school’s new program.

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