Fetching the paper…
Reading the bibliography…
Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the graph-structured and geometric aspects of the problem domain.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
Earlier work this paper cites.
Native protein sequences are close to optimal for their structures
Brian Kuhlman and David Baker · 2000
Earlier work this paper cites.
Processing and evaluation of predictions in casp4
Adam Zemla, Česlovas Venclovas, John Moult, and Krzysztof Fidelis · 2001
Earlier work this paper cites.
Biochemistry, W.H. Freeman and Company, 2002
Jeremy M Berg, John L Tymoczko, and Lubert Stryer · 2002
Earlier work this paper cites.
Relating protein motion to catalysis
Sharon Hammes-Schiffer and Stephen J Benkovic · 2006
Earlier work this paper cites.
Macromolecular modeling with rosetta
Rhiju Das and David Baker · 2008
Earlier work this paper cites.
Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles
Zhixiu Li, Yuedong Yang, Eshel Faraggi, Jian Zhan, and Yaoqi Zhou · 2014
Earlier work this paper cites.
Protein single-model quality assessment by feature-based probability density functions
Renzhi Cao and Jianlin Cheng · 2016
Earlier work this paper cites.
Boosting docking-based virtual screening with deep learning
Janaina Cruz Pereira, Ernesto Raul Caffarena, and Cicero Nogueira dos Santos · 2016
Earlier work this paper cites.
Inductive bias of deep convolutional networks through pooling geometry
Nadav Cohen and Amnon Shashua · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Voromqa: Assessment of protein structure quality using interatomic contact areas
Kliment Olechnovič and Česlovas Venclovas · 2017
Earlier work this paper cites.
Proq3d: improved model quality assessments using deep learning
Karolis Uziela, David Menéndez Hurtado, Nanjiang Shu, Björn Wallner, and Arne Elofsson · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Cited alongside, same era.
Deep convolutional networks for quality assessment of protein folds
Georgy Derevyanko, Sergei Grudinin, Yoshua Bengio, and Guillaume Lamoureux · 2018
Cited alongside, same era.
Design of metalloproteins and novel protein folds using variational autoencoders
Joe G Greener, Lewis Moffat, and David T Jones · 2018
Cited alongside, same era.
Deep transfer learning in the assessment of the quality of protein models
David Menéndez Hurtado, Karolis Uziela, and Arne Elofsson · 2018
Cited alongside, same era.
Spin2: Predicting sequence profiles from protein structures using deep neural networks
James O’Connell, Zhixiu Li, Jack Hanson, Rhys Heffernan, James Lyons, Kuldip Paliwal, Abdollah Dehzangi, Yuedong Yang, and Yaoqi Zhou · 2018
Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2019
Later among the works it cites.
Protein model quality assessment using 3d oriented convolutional neural networks
Guillaume Pagès, Benoit Charmettant, and Sergei Grudinin · 2019
Later among the works it cites.
A structure-based deep learning framework for protein engineering
Raghav Shroff, Austin W Cole, Barrett R Morrow, Daniel J Diaz, Isaac Donnell, Jimmy Gollihar, Andrew D Ellington, and Ross Thyer · 2019
Later among the works it cites.
End-to-end learning on 3d protein structure for interface prediction
Raphael Townshend, Rishi Bedi, Patricia Suriana, and Ron Dror · 2019
Later among the works it cites.
Assessment of protein model structure accuracy estimation in casp13: Challenges in the era of deep learning
Jonghun Won, Minkyung Baek, Bohdan Monastyrskyy, Andriy Kryshtafovych, and Chaok Seok · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Cited alongside, same era.
Computational protein design with deep learning neural networks
Jingxue Wang, Huali Cao, John ZH Zhang, and Yifei Qi · 2018
Cited alongside, same era.
Cormorant: Covariant molecular neural networks
Brandon Anderson, Truong-Son Hy, and Risi Kondor · 2019
Cited alongside, same era.
Graphqa: Protein model quality assessment using graph convolutional network
Federico Baldassarre, David Menéndez Hurtado, Arne Elofsson, and Hossein Azizpour · 2019
Cited alongside, same era.
To improve protein sequence profile prediction through image captioning on pairwise residue distance map
Sheng Chen, Zhe Sun, Lihua Lin, Zifeng Liu, Xun Liu, Yutian Chong, Yutong Lu, Huiying Zhao, and Yuedong Yang · 2019
Cited alongside, same era.
Estimation of model accuracy in casp13
Jianlin Cheng, Myong-Ho Choe, Arne Elofsson, Kun-Sop Han, Jie Hou, Ali HA Maghrabi, Liam J McGuffin, David Menéndez-Hurtado, Kliment Olechnovič, Torsten Schwede, et al · 2019
Cited alongside, same era.
Deep convolutional neural networks for predicting the quality of single protein structural models
Jie Hou, Renzhi Cao, and Jianlin Cheng · 2019
Cited alongside, same era.
Yuan Zhang, Yang Chen, Chenran Wang, Chun-Chao Lo, Xiuwen Liu, Wei Wu, and Jinfeng Zhang · 2019
Later among the works it cites.
Protein sequence design with a learned potential
Namrata Anand, Raphael Ryuichi Eguchi, Alexander Derry, Russ B Altman, and Possu Huang · 2020
Closest in time.
GraphQA: protein model quality assessment using graph convolutional networks
Federico Baldassarre, David Menéndez Hurtado, Arne Elofsson, and Hossein Azizpour · 2020
Closest in time.
Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes
Stephan Eismann, Raphael JL Townshend, Nathaniel Thomas, Milind Jagota, Bowen Jing, and Ron O Dror · 2020
Closest in time.
A review of deep learning methods for antibodies
Jordan Graves, Jacob Byerly, Eduardo Priego, Naren Makkapati, S Vince Parish, Brenda Medellin, and Monica Berrondo · 2020
Closest in time.
Densecpd: Improving the accuracy of neural-network-based computational protein sequence design with densenet
Yifei Qi and John ZH Zhang · 2020
Closest in time.
Fast and flexible protein design using deep graph neural networks
Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, and Philip M Kim · 2020
Closest in time.
Qmeandisco—distance constraints applied on model quality estimation
Gabriel Studer, Christine Rempfer, Andrew M Waterhouse, Rafal Gumienny, Juergen Haas, and Torsten Schwede · 2020
Closest in time.