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The Future of Drug Development with Quantum Computing

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High Performance Computing for Drug Discovery and Biomedicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2716))

Abstract

Novel medication development is a time-consuming and expensive multistage procedure. Recent technology developments have lowered timeframes, complexity, and cost dramatically. Current research projects are driven by AI and machine learning computational models. This chapter will introduce quantum computing (QC) to drug development issues and provide an in-depth discussion of how quantum computing may be used to solve various drug discovery problems. We will first discuss the fundamentals of QC, a review of known Hamiltonians, how to apply Hamiltonians to drug discovery challenges, and what the noisy intermediate-scale quantum (NISQ) era methods and their limitations are.

We will further discuss how these NISQ era techniques can aid with specific drug discovery challenges, including protein folding, molecular docking, AI−/ML-based optimization, and novel modalities for small molecules and RNA secondary structures. Consequently, we will discuss the latest QC landscape’s opportunities and challenges.

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Abbreviations

ADMET:

Absorption, distribution, metabolism, excretion, and toxicity

DNA:

Deoxyribonucleic acid

GAN:

Generative adversarial networks

NISQ:

Noisy intermediate-scale quantum

QA:

Quantum annealing

QC:

Quantum compute

QAOA:

Quantum approximate optimization algorithm

QBM:

Quantum Boltzmann machine

QGM:

Quantum generative adversarial network model

QGA:

Quantum genetic algorithms

QuANN:

Quantum artificial neural network

QMCTS:

Quantum Monte Carlo tree search

QMC:

Quantum Monte Carlo

QMD:

Quantum molecular dynamics

QML:

Quantum machine learning

QPCA:

Quantum principle component analysis

QPE:

Quantum phase estimation

QSVM:

Quantum support vector machine

QVC:

Quantum variational classifier

QW:

Quantum walk

SNP:

Single nucleotide polymorphism

TF:

Transcription factor

VQE:

Variational quantum eigensolver

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Authors and Affiliations

  1. Evotec (UK) Ltd., Oxfordshire, UK

    Bhushan Bonde & Pratik Patil

  2. Digital Futures Institute, University of Suffolk, Ipswich, UK

    Bhushan Bonde, Pratik Patil & Bhaskar Choubey

  3. Chair of Analogue Circuits and Image Sensors, Siegen University, Siegen, Germany

    Bhaskar Choubey

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  1. Bhushan Bonde
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  2. Pratik Patil
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  3. Bhaskar Choubey
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Correspondence to Bhushan Bonde .

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  1. In Silico Research and Development, Evotec UK Ltd, Abingdon, UK

    Alexander Heifetz

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Bonde, B., Patil, P., Choubey, B. (2024). The Future of Drug Development with Quantum Computing. In: Heifetz, A. (eds) High Performance Computing for Drug Discovery and Biomedicine. Methods in Molecular Biology, vol 2716. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3449-3_7

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  • DOI: https://doi.org/10.1007/978-1-0716-3449-3_7

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