Fetching the paper…
Reading the bibliography…
We conduct an extensive study on using near-term quantum computers for a task in the domain of computational biology.
A pattern search method for putative anchor residues in T cell epitopes
U Hobohm and A Meyerhans · 1993
Earlier work this paper cites.
Quantitation of peptide anchor residue contributions to class I major histocompatibility complex molecule binding
Y Saito, P A Peterson, and M Matsumura · 1993
Earlier work this paper cites.
Importance of peptide amino and carboxyl termini to the stability of MHC class I molecules
M Bouvier and D C Wiley · 1994
Earlier work this paper cites.
Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
From antigen processing to peptide-MHC binding
Emil R Unanue · 2006
Earlier work this paper cites.
Recent advances in antigen processing and presentation
Peter E Jensen · 2007
Earlier work this paper cites.
Major histocompatibility complex class I binding predictions as a tool in epitope discovery
Claus Lundegaard, Ole Lund, Søren Buus, and Morten Nielsen · 2010
Earlier work this paper cites.
PepBind: a comprehensive database and computational tool for analysis of protein-peptide interactions
Arindam Atanu Das, Om Prakash Sharma, Muthuvel Suresh Kumar, Ramadas Krishna, and Premendu P Mathur · 2013
Earlier work this paper cites.
A peptide’s perspective on antigen presentation to the immune system
Jacques Neefjes and Huib Ovaa · 2013
Earlier work this paper cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Machine learning methods for predicting HLA-peptide binding activity
Heng Luo, Hao Ye, Hui Wen Ng, Leming Shi, Weida Tong, Donna L Mendrick, and Huixiao Hong · 2015
Cited alongside, same era.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Deepmhc: Deep convolutional neural networks for high-performance peptide-mhc binding affinity prediction
Jianjun Hu and Zhonghao Liu · 2017
Cited alongside, same era.
Major histocompatibility complex (MHC) class I and MHC class II proteins: Conformational plasticity in antigen presentation
Marek Wieczorek, Esam T Abualrous, Jana Sticht, Miguel Álvaro-Benito, Sebastian Stolzenberg, Frank Noé, and Christian Freund · 2017
Cited alongside, same era.
General prediction of peptide-MHC binding modes using incremental docking: A proof of concept
Dinler A Antunes, Didier Devaurs, Mark Moll, Gregory Lizée, and Lydia E Kavraki · 2018
Cited alongside, same era.
Captum: A unified and generic model interpretability library for pytorch, 2020
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson · 2020
Later among the works it cites.
Unconventional peptide presentation by classical MHC class I and implications for T and NK cell activation
Dirk M Zajonc · 2020
Later among the works it cites.
Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
Later among the works it cites.
Volumetric benchmarking of error mitigation with qermit, 2022
Cristina Cîrstoiu, Silas Dilkes, Daniel Mills, Seyon Sivarajah, and Ross Duncan · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The immune epitope database (iedb): 2018 update
Randi Vita, Swapnil Mahajan, James A Overton, Sandeep Kumar Dhanda, Sheridan Martini, Jason R Cantrell, Daniel K Wheeler, Alessandro Sette, and Bjoern Peters · 2018
Cited alongside, same era.
On the qubit routing problem
Alexander Cowtan, Silas Dilkes, Ross Duncan, Alexandre Krajenbrink, Will Simmons, and Seyon Sivarajah · 2019
Cited alongside, same era.
Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum‐classical algorithms
Sukin Sim, Peter D. Johnson, and Alán Aspuru‐Guzik · 2019
Cited alongside, same era.
Improved modeling of peptide-protein binding through global docking and accelerated molecular dynamics simulations
Jinan Wang, Andrey Alekseenko, Dima Kozakov, and Yinglong Miao · 2019
Cited alongside, same era.
Expressive power of parametrized quantum circuits
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, and Dacheng Tao · 2020
Cited alongside, same era.
Ian W Hamley · 2022
Later among the works it cites.
Improving MHC class I antigen-processing predictions using representation learning and cleavage site-specific kernels
Patrick J Lawrence and Xia Ning · 2022
Later among the works it cites.
Quantumnas: Noise-adaptive search for robust quantum circuits
Hanrui Wang, Yongshan Ding, Jiaqi Gu, Zirui Li, Yujun Lin, David Z Pan, Frederic T Chong, and Song Han · 2022
Later among the works it cites.
Qnlp in practice: Running compositional models of meaning on a quantum computer
Robin Lorenz, Anna Pearson, Konstantinos Meichanetzidis, Dimitri Kartsaklis, and Bob Coecke · 2023
Closest in time.
Grammar-aware sentence classification on quantum computers
Konstantinos Meichanetzidis, Alexis Toumi, Giovanni de Felice, and Bob Coecke · 2023
Closest in time.
Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 2058
Closest in time.