Quantum computing's potential for drug discovery: Early stage industry dynamics

Zinner, Maximillian; Dahlhausen, Florian; Boehme, Philip; Ehlers, Jan P.; Bieske, Linn; Fehring, Leonard

doi:10.1016/j.drudis.2021.06.003

Cited by 31 publications

(19 citation statements)

References 30 publications

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“…Beyond the precincts of academic research, even the interest of industrial players on quantum-enabled technologies seems to be escalating rapidly. The report by Zinner et al 687 has identified that 17 out of 21 established pharmaceutical companies and 38 start-ups are directly working on enhancing and ameliorating the technical challenges in the drug discovery pipeline using quantum computers. 75% of such companies so far have been identified to be geographically in Europe and North America.…”

Section: Drug-discovery Pipeline and Pharmaceutical Applicationsmentioning

confidence: 99%

“…75% of such companies so far have been identified to be geographically in Europe and North America. The cumulative funding received by all the start-ups as per the report 687 is h311 million with the top five funded start-ups being Cambridge Quantum Computing, Zapata, 1QBit, Quantum Biosystems and SeeQC. Most of the activity is directed towards virtual screening for ligand-based drug designing protocol and subsequent lead optimization.…”

Section: Drug-discovery Pipeline and Pharmaceutical Applicationsmentioning

confidence: 99%

“…The consortium is known to have a collaboration with Quantum Economic Development Consortium (QED-C). 687 Hardware needs can be sorted through collaborations with end-to-end technological providers like Google, IBM, Honeywell, Rigetti, Xanadu, etc. each of which have quantum computers of varying architecture and even on different platforms and have even promised to develop larger scale ones in the near future.…”

Section: Drug-discovery Pipeline and Pharmaceutical Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

Quantum machine learning for chemistry and physics

Sajjan

Selvarajan

et al. 2022

Chem. Soc. Rev.

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show abstract

Section: Drug-discovery Pipeline and Pharmaceutical Applicationsmentioning

confidence: 99%

Section: Drug-discovery Pipeline and Pharmaceutical Applicationsmentioning

confidence: 99%

Section: Drug-discovery Pipeline and Pharmaceutical Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Quantum machine learning for chemistry and physics

Sajjan

Selvarajan

et al. 2022

Chem. Soc. Rev.

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show abstract

“…While practical quantum supremacy is still under debate, there is certainly enough evidence that quantum computing could be better than analogous classical computing in some aspects in the near future. In fact, many institutions have started to explore QC as applied to drug discovery, with near-term focuses on selected tasks such as lead optimization or compound screening (Zinner et. al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Insights from Incorporating Quantum Computing into Drug Design Workflows

Lau¹,

Emani

Chapman

et al. 2022

Preprint

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While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the SARS-CoV-2 protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD.

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“…Quantum computers use certain quantum mechanical phenomena such as superposition and entanglement to attain a substantial speedup over their conventional classical counterparts [1–3]. Therefore, they are speculated to help solve certain problems that are intractable for even the most powerful classical supercomputers in the areas of drug discovery [4], artificial intelligence [5], material simulations [6] etc. However, the current generation of quantum hardware, generally referred to as noisy intermediate‐scale quantum (NISQ) hardware [7], has limited computational capabilities due to a small number of qubits, restrictive hardware connectivity, and poor qubit quality [7].…”

Section: Introductionmentioning

confidence: 99%

Surface code design for asymmetric error channels

Azad

Lipińska

Shilpa

et al. 2022

IET Quantum Communication

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Surface codes are quantum error correcting codes typically defined on a 2D array of qubits. A [dx, dz] surface code design is being introduced, where dx(dz) represents the distance of the code for bit (phase) error correction, motivated by the fact that the severity of bit flip and phase flip errors in the physical quantum system is asymmetric. We present pseudo‐threshold and threshold values for the proposed surface code design for asymmetric error channels in the presence of various degrees of asymmetry of PauliX^,trueY^ $\text{Pauli}\,\hat{X},\,\hat{Y}$, andZ^ $\text{and}\,\hat{Z}$ errors in a depolarisation channel. We demonstrate that compared to symmetric surface codes, our asymmetric surface codes can provide almost double the pseudo‐threshold rates while requiring less than half the number of physical qubits in the presence of increasing asymmetry in the error channel. Our results show that for low degree of asymmetry, it is advantageous to increase dx along with dz. However, as the asymmetry of the channel increases, higher pseudo‐threshold is obtained with increasing dz when dx is kept constant at a low value. Additionally, we also show that the advantage in the pseudo‐threshold rates begins to saturate for any possible degree of asymmetry in the error channel as the surface code asymmetry is continued to be increased.

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Quantum computing's potential for drug discovery: Early stage industry dynamics

Cited by 31 publications

References 30 publications

Quantum machine learning for chemistry and physics

Quantum machine learning for chemistry and physics

Insights from Incorporating Quantum Computing into Drug Design Workflows

Surface code design for asymmetric error channels

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