AI’s Next Wave

HMS researcher Ben Gyori on the future of human-machine collaboration in scientific research

In 2019 alone, more than 1.3 million new citations were added to the 30 million existing abstracts and articles catalogued by PubMed, the NIH’s database of biomedical and life sciences journals and literature. Each new entry, for the most part, contributes to the sum total of knowledge produced and validated by the world’s life sciences community. Every entry, however, also serves as a reminder of how much remains to be understood about the astonishingly complex science of biology—from the intricate networks of biomolecules and molecular machines that underlie all of life’s processes to how their myriad interactions shape the behaviors of everything from cells and tissues to organisms and ecosystems.




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Reverse engineering these processes gives scientists the best chance to understand human health and intervene in disease, but the human brain simply cannot keep up with this overwhelming volume of information.In this era of big data, it is no wonder then that machine learning and other artificial intelligence (AI) methods, with a beyond-human ability to identify the subtlest patterns and connections in data at scale, have become essential tools in the quest to untangle the Gordian knot that is biology.

But what about the next era? For a group of researchers at Harvard Medical School’s Laboratory of Systems Pharmacology (LSP), a multi-disciplinary, cross-institutional effort to reinvent the science underlying the development of new medicines, the future utility of AI may not be as just a tool.Instead, they are working to enable meaningful collaboration between humans and machines—using an AI system that reads essentially everything in PubMed and automates scientific discovery.



Developed by a team led by Benjamin Gyori and John Bachman, both research associates in therapeutic science at the LSP, and Peter Sorger, the Otto Krayer Professor of Systems Pharmacology at HMS and director of the LSP, the system text-mines enormous volumes of scientific literature. It then extracts information about causal mechanisms, creates models and generates predictions about biological interactions that human scientists can go on to test.

Earlier this fall, Gyori received a young faculty award from the U.S. Defense Advanced Research Projects Agency (DARPA) to advance their ambitious efforts. Moving toward what the agency dubs the third wave of AI, Gyori and colleagues aim for their AI method to soon be capable of learning and creating explanations based on contextual reasoning—similar to how human brains work. Harvard Medicine News spoke with Gyori about his vision for the future of AI in scientific research.

#AIMolecularInteractions#MolecularBiology#AIinBiology#DrugDiscovery#Bioinformatics#MolecularResearch #AIinScience #Biotech #Genomics #ComputationalBiology

Website Link: https://molecularbiologist.org/

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