Researchers recently published findings that could lay the groundwork for applying quantum computing methods to protein structure prediction.
that could lay the groundwork for applying quantum computing methods to protein structure prediction. This publication is the first peer-reviewed quantum computing paper from the Cleveland Clinic-IBM Discovery Accelerator partnership.
In recent years, machine learning techniques have made significant progress in protein structure prediction. These methods are reliant on training data to make predictions. This means that they are constrained by how many proteins they have been taught to recognize. This can lead to lower levels of accuracy when the programs/algorithms encounter a protein that is mutated or very different from those on which they were trained, which is common with genetic disorders.
To help overcome these limitations, the research team applied a mix of quantum and classical computing methods. This framework could allow quantum algorithms to address the areas that are challenging for state-of-the-art classical computing, including protein size, intrinsic disorder, mutations and the physics involved in proteins folding.
"One of the most unique things about this project is the number of disciplines involved," says Dr. Raubenolt."Our team's expertise ranges from computational biology and chemistry, structural biology, software and automation engineering, to experimental atomic and nuclear physics, mathematics, and of course quantum computing and algorithm design.
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