Abstract
AbstractQuantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to “hype,” and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Computer Algorithms and Programming Electronic Structure Theory > Ab Initio Electronic Structure Methods
Highlights
Since the advent of modern computing, algorithms and mathematical models have been used to help solve biological problems, from exploring the complexity of the human genome to modeling the behavior of biomolecules
In genomics, annotation of gene information has made extensive use of hidden Markov models (HMMs) [59]; in drug discovery, a vast array of statistical models have been developed to estimate molecular properties, or to predict if a ligand will bind to a protein [60]; and, in structural biology, deep neural networks have been used to both predict contacts [61], secondary structure [62] and most recently 3D protein structures [63]
Our introduction is aimed at developments that are likely to be directly applicable in biology; for a wider treatment of quantum machine learning, see [23,24,25]
Summary
Since the advent of modern computing, algorithms and mathematical models have been used to help solve biological problems, from exploring the complexity of the human genome to modeling the behavior of biomolecules. The best algorithms for problems like predicting the folding of a protein, calculating the binding affinity of a ligand for a macromolecule, or finding optimal large-scale genomic alignments require computational resources that are beyond even the most powerful supercomputers of our era. The solution to these challenges may lie in a paradigm shift in computing. Computing the full electronic wavefunction of an average drug molecule numerically is expected to take longer than the age of the universe on any current supercomputer using conventional algorithms [14], while even a modest-sized quantum computer may be able to solve this in a timescale of days Motivated by this promise of quantum advantage, the quest to build a quantum processor is ongoing. The technical difficulties in manufacturing, controlling and protecting quantum systems from noise are staggering, and the first prototypes have only appeared in the last decade
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