Penn State associate professor of electrical engineering and computer science Swaroop Ghosh said that current AI models are limited by current classical computing processing power.
“Quantum AI models are claimed to be more expressive compared to the classical neural networks; in other words, they have a higher capability to approximate the desired functionality compared to the classical AI models of similar scale,” said Ghosh.
“Quantum computers bring effective sampling capabilities so they may be able to model useful distributions of drug-like molecules more efficiently than classical computers. Evolution of quantum primitives, such as quantum memory, can offer further speedup for machine learning tasks since the training data can be directly processed in the quantum domain.”
“Drug discovery is a lengthy process that can span a decade and costs billions of dollars,” said Dokholyan.
“Currently, the pace of Federal Drug Administration (FDA) approval of new compounds is only about 40 novel compounds per year. Accelerating drug discovery by computationally screening a massive number of compounds promises to significantly reduce the costs and time for finding effective new cures against diseases. Unlike the traditional computational drug screening approaches that target libraries of billions of compounds, utilizing Quantum Computers along with novel AI-driven algorithms promise to cover a vastly larger chemical space.”
One particular use case for the research will be the development of drugs that inhibit Ras proteins, a family of proteins that can cause cancer if they are overactive. The team will use existing quantum computing technology accessible via the cloud, such as IBM and Microsoft.
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