QFold: quantum walks and deep learning to solve protein folding

Casares, Pablo Antonio Moreno; Campos, Roberto; Martín-Delgado, M. A.

doi:10.1088/2058-9565/ac4f2f

Cited by 19 publications

(11 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gate-based quantum algorithms are also being proposed to address biopolymer conformations, including a hybrid approach combining quantum Monte Carlo and machine learning for protein structure prediction [ 80 ], another quantum Monte Carlo approach to antibody loop modelling [ 54 ] and protein design exploiting Grover’s algorithm [ 81 ].…”

Section: Computational Approaches In Life Science: From Classical To ...mentioning

confidence: 99%

Quantum computing algorithms: getting closer to critical problems in computational biology

Marchetti

Nifosı̀

Martelli

et al. 2022

Briefings in Bioinformatics

View full text Add to dashboard Cite

The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.

show abstract

Section: Computational Approaches In Life Science: From Classical To ...mentioning

confidence: 99%

Quantum computing algorithms: getting closer to critical problems in computational biology

Marchetti

Nifosı̀

Martelli

et al. 2022

Briefings in Bioinformatics

View full text Add to dashboard Cite

show abstract

“…[203,204] All these classes of optimization algorithms can be, in principle, applied to biochemical problems such as those mentioned above. The first attempts to solve the protein folding optimization problem relied on quantum annealing [205,206] while, more recently, hybrid classical-variational algorithms based on QAOA, [109] VQE, [110] and on quantum random walks [207] have been proposed. The efficiency of all these methods has only been explored so far for simplified lattice models of small polypeptides with a few dozen of amino acids.…”

Section: Quantum Computing For Structural Biologymentioning

confidence: 99%

Quantum Computing for Molecular Biology**

2023

View full text Add to dashboard Cite

Molecular biology and biochemistry interpret microscopic processes in the living world in terms of molecular structures and their interactions, which are quantum mechanical by their very nature. Whereas the theoretical foundations of these interactions are well established, the computational solution of the relevant quantum mechanical equations is very hard. However, much of molecular function in biology can be understood in terms of classical mechanics, where the interactions of electrons and nuclei have been mapped onto effective classical surrogate potentials that model the interaction of atoms or even larger entities. The simple mathematical structure of these potentials offers huge computational advan-tages; however, this comes at the cost that all quantum correlations and the rigorous many-particle nature of the interactions are omitted. In this work, we discuss how quantum computation may advance the practical usefulness of the quantum foundations of molecular biology by offering computational advantages for simulations of biomolecules. We not only discuss typical quantum mechanical problems of the electronic structure of biomolecules in this context, but also consider the dominating classical problems (such as protein folding and drug design) as well as data-driven approaches of bioinformatics and the degree to which they might become amenable to quantum simulation and quantum computation.

show abstract

“…Explorations of variational QML approaches for biology and medicine are only now getting under way. They include proof of principle implementations of protein folding with a hybrid deep learning approach leveraging quantum walks on a gate-based superconducting device [ 213 ] and the diagnosis of breast cancer from legacy clinical data via QKE [ 214 ]. As larger, more flexible quantum devices are made available, further growth of research into applications of variational QML is expected.…”

Section: The Pursuit Of Nisq Advantagesmentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier

Sawaya

Guerreschi

et al. 2022

J. R. Soc. Interface.

View full text Add to dashboard Cite

Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

show abstract

QFold: quantum walks and deep learning to solve protein folding

Cited by 19 publications

References 59 publications

Quantum computing algorithms: getting closer to critical problems in computational biology

Quantum computing algorithms: getting closer to critical problems in computational biology

Quantum Computing for Molecular Biology**

Biology and medicine in the landscape of quantum advantages

Contact Info

Product

Resources

About