Fetching the paper…
Reading the bibliography…
By folding into particular 3D structures, proteins play a key role in living beings.
Jelinek, F., Mercer, R.L., Bahl, L.R., Baker, J.K.: Perplexity—a measure of the difficulty of speech recognition tasks. The Journal of the Acoustical Society of America 62
1977
Earlier work this paper cites.
Webb, E.C., et al.: Enzyme nomenclature 1992. Recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology on the Nomenclature and Classification of Enzymes. Academic Press (1992)
1992
Earlier work this paper cites.
Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: Scop: a structural classification of proteins database for the investigation of sequences and structures. Journal of molecular biology 247
1995
Earlier work this paper cites.
Orengo, C.A., Michie, A.D., Jones, S., Jones, D.T., Swindells, M.B., Thornton, J.M.: Cath–a hierarchic classification of protein domain structures. Structure 5
1997
Earlier work this paper cites.
Lundström, J., Rychlewski, L., Bujnicki, J., Elofsson, A.: Pcons: A neural-network–based consensus predictor that improves fold recognition. Protein science 10
2001
Earlier work this paper cites.
Schaap, M.G., Leij, F.J., Van Genuchten, M.T.: Rosetta: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of hydrology 251
2001
Earlier work this paper cites.
Duhovny, D., Nussinov, R., Wolfson, H.J.: Efficient unbound docking of rigid molecules. In: International workshop on algorithms in bioinformatics. pp. 185–200. Springer (2002)
2002
Earlier work this paper cites.
Zemla, A.: Lga: a method for finding 3d similarities in protein structures. Nucleic acids research 31
2003
Earlier work this paper cites.
Rohl, C.A., Strauss, C.E., Misura, K.M., Baker, D.: Protein structure prediction using rosetta. Methods in enzymology 383
2004
Earlier work this paper cites.
Shulman-Peleg, A., Nussinov, R., Wolfson, H.J.: Recognition of functional sites in protein structures. Journal of molecular biology 339
2004
Earlier work this paper cites.
Xu, J., Berger, B.: Fast and accurate algorithms for protein side-chain packing. Journal of the ACM (JACM) 53
2006
Earlier work this paper cites.
Nelson, D.L., Lehninger, A.L., Cox, M.M.: Lehninger principles of biochemistry. Macmillan (2008)
2008
Earlier work this paper cites.
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Icml (2010)
2010
Earlier work this paper cites.
Voet, D., Voet, J.G.: Biochemistry. John Wiley & Sons (2010)
2010
Earlier work this paper cites.
Zhang, J., Zhang, Y.: A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction. PloS one 5
2010
Earlier work this paper cites.
Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35
2012
Earlier work this paper cites.
Källberg, M., Wang, H., Wang, S., Peng, J., Wang, Z., Lu, H., Xu, J.: Template-based protein structure modeling using the raptorx web server. Nature protocols 7
2012
Earlier work this paper cites.
Li, Z., Yang, Y., Faraggi, E., Zhan, J., Zhou, Y.: Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles. Proteins: Structure, Function, and Bioinformatics 82
2014
Earlier work this paper cites.
Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T., Tramontano, A.: Critical assessment of methods of protein structure prediction (casp)—round x. Proteins: Structure, Function, and Bioinformatics 82
2014
Earlier work this paper cites.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15
2014
Earlier work this paper cites.
Cohen, T., Welling, M.: Group equivariant convolutional networks. In: International conference on machine learning. pp. 2990–2999. PMLR (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Alford, R.F., Leaver-Fay, A., Jeliazkov, J.R., O’Meara, M.J., DiMaio, F.P., Park, H., Shapovalov, M.V., Renfrew, P.D., Mulligan, V.K., Kappel, K., et al.: The rosetta all-atom energy function for macromolecular modeling and design. Journal of chemical theory and computation 13
2017
Earlier work this paper cites.
Fout, A.M.: Protein interface prediction using graph convolutional networks. Ph.D. thesis, Colorado State University (2017)
2017
Earlier work this paper cites.
Olechnovič, K., Venclovas, Č.: Voromqa: Assessment of protein structure quality using interatomic contact areas. Proteins: Structure, Function, and Bioinformatics 85
2017
Earlier work this paper cites.
Schütt, K., Kindermans, P.J., Sauceda Felix, H.E., Chmiela, S., Tkatchenko, A., Müller, K.R.: Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Torng, W., Altman, R.B.: 3d deep convolutional neural networks for amino acid environment similarity analysis. BMC bioinformatics 18
2017
Earlier work this paper cites.
Uziela, K., Menendez Hurtado, D., Shu, N., Wallner, B., Elofsson, A.: Proq3d: improved model quality assessments using deep learning. Bioinformatics 33
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
Cited alongside, same era.
Amidi, A., Amidi, S., Vlachakis, D., Megalooikonomou, V., Paragios, N., Zacharaki, E.I.: Enzynet: enzyme classification using 3d convolutional neural networks on spatial representation. PeerJ 6
2018
Cited alongside, same era.
Bepler, T., Berger, B.: Learning protein sequence embeddings using information from structure. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Derevyanko, G., Grudinin, S., Bengio, Y., Lamoureux, G.: Deep convolutional networks for quality assessment of protein folds. Bioinformatics 34
2018
Cited alongside, same era.
Jing, B., Eismann, S., Suriana, P., Townshend, R.J.L., Dror, R.: Learning from protein structure with geometric vector perceptrons. In: International Conference on Learning Representations (2020)
2020
Later among the works it cites.
Köhler, J., Klein, L., Noé, F.: Equivariant flows: exact likelihood generative learning for symmetric densities. In: International Conference on Machine Learning. pp. 5361–5370. PMLR (2020)
2020
Later among the works it cites.
Qi, Y., Zhang, J.Z.: Densecpd: improving the accuracy of neural-network-based computational protein sequence design with densenet. Journal of chemical information and modeling 60
2020
Later among the works it cites.
Strodthoff, N., Wagner, P., Wenzel, M., Samek, W.: Udsmprot: universal deep sequence models for protein classification. Bioinformatics 36
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
O’Connell, J., Li, Z., Hanson, J., Heffernan, R., Lyons, J., Paliwal, K., Dehzangi, A., Yang, Y., Zhou, Y.: Spin2: Predicting sequence profiles from protein structures using deep neural networks. Proteins: Structure, Function, and Bioinformatics 86
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Wang, J., Cao, H., Zhang, J.Z., Qi, Y.: Computational protein design with deep learning neural networks. Scientific reports 8
2018
Cited alongside, same era.
Weiler, M., Geiger, M., Welling, M., Boomsma, W., Cohen, T.S.: 3d steerable cnns: Learning rotationally equivariant features in volumetric data. Advances in Neural Information Processing Systems 31
2018
Cited alongside, same era.
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. Advances in neural information processing systems 31
2018
Cited alongside, same era.
Strokach, A., Becerra, D., Corbi-Verge, C., Perez-Riba, A., Kim, P.M.: Fast and flexible protein design using deep graph neural networks. Cell Systems 11
2020
Later among the works it cites.
Zhang, Y., Chen, Y., Wang, C., Lo, C.C., Liu, X., Wu, W., Zhang, J.: Prodconn: Protein design using a convolutional neural network. Proteins: Structure, Function, and Bioinformatics 88
2020
Later among the works it cites.
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G.R., Wang, J., Cong, Q., Kinch, L.N., Schaeffer, R.D., et al.: Accurate prediction of protein structures and interactions using a three-track neural network. Science 373
2021
Later among the works it cites.
Baldassarre, F., Menéndez Hurtado, D., Elofsson, A., Azizpour, H.: Graphqa: protein model quality assessment using graph convolutional networks. Bioinformatics 37
2021
Later among the works it cites.
2021
Later among the works it cites.
Cao, Y., Das, P., Chenthamarakshan, V., Chen, P.Y., Melnyk, I., Shen, Y.: Fold2seq: A joint sequence (1d)-fold (3d) embedding-based generative model for protein design. In: International Conference on Machine Learning. pp. 1261–1271. PMLR (2021)
2021
Later among the works it cites.
Deng, C., Litany, O., Duan, Y., Poulenard, A., Tagliasacchi, A., Guibas, L.J.: Vector neurons: A general framework for so (3)-equivariant networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 12200–12209 (2021)
2021
Later among the works it cites.
Eismann, S., Townshend, R.J., Thomas, N., Jagota, M., Jing, B., Dror, R.O.: Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes. Proteins: Structure, Function, and Bioinformatics 89
2021
Later among the works it cites.
Gligorijević, V., Renfrew, P.D., Kosciolek, T., Leman, J.K., Berenberg, D., Vatanen, T., Chandler, C., Taylor, B.C., Fisk, I.M., Vlamakis, H., et al.: Structure-based protein function prediction using graph convolutional networks. Nature communications 12
2021
Later among the works it cites.
Hermosilla Casajus, P., Schäfer, M., Lang, M., Fackelmann, G., Vázquez Alcocer, P.P., Kozliková, B., Krone, M., Ritschel, T., Ropinski, T.: Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures. In: International Conference on Learning Representations, ICLR 2021: Vienna, Austria, May 04 2021. pp. 1–16. OpenReview. net (2021)
2021
Later among the works it cites.
Igashov, I., Olechnovič, K., Kadukova, M., Venclovas, Č., Grudinin, S.: Vorocnn: deep convolutional neural network built on 3d voronoi tessellation of protein structures. Bioinformatics 37
2021
Later among the works it cites.
Jing, B., Eismann, S., Soni, P.N., Dror, R.O.: Equivariant graph neural networks for 3d macromolecular structure. ICML 2021 CompBio Workshop (2021)
2021
Later among the works it cites.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al.: Highly accurate protein structure prediction with alphafold. Nature 596
2021
Later among the works it cites.
Kwon, S., Won, J., Kryshtafovych, A., Seok, C.: Assessment of protein model structure accuracy estimation in casp14: Old and new challenges. Proteins: Structure, Function, and Bioinformatics (2021)
2021
Later among the works it cites.
Li, S., Zhou, J., Xu, T., Huang, L., Wang, F., Xiong, H., Huang, W., Dou, D., Xiong, H.: Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. pp. 975–985 (2021)
2021
Later among the works it cites.
Liu, Y., Wang, L., Liu, M., Zhang, X., Oztekin, B., Ji, S.: Spherical message passing for 3d graph networks (2021)
2021
Later among the works it cites.
Satorras, V.G., Hoogeboom, E., Welling, M.: E (n) equivariant graph neural networks. In: International conference on machine learning. pp. 9323–9332. PMLR (2021)
2021
Later among the works it cites.
Schütt, K., Unke, O., Gastegger, M.: Equivariant message passing for the prediction of tensorial properties and molecular spectra. In: International Conference on Machine Learning. pp. 9377–9388. PMLR (2021)
2021
Later among the works it cites.
Sverrisson, F., Feydy, J., Correia, B.E., Bronstein, M.M.: Fast end-to-end learning on protein surfaces. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15272–15281 (2021)
2021
Later among the works it cites.
Townshend, R.J.L., Vögele, M., Suriana, P.A., Derry, A., Powers, A., Laloudakis, Y., Balachandar, S., Jing, B., Anderson, B.M., Eismann, S., et al.: Atom3d: Tasks on molecules in three dimensions. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2021)
2021
Later among the works it cites.
Vecchio, A., Deac, A., Liò, P., Veličković, P.: Neural message passing for joint paratope-epitope prediction (2021)
2021
Later among the works it cites.
Wang, X., Flannery, S.T., Kihara, D.: Protein docking model evaluation by graph neural networks. Frontiers in Molecular Biosciences 8
2021
Later among the works it cites.
Luo, S., Li, J., Guan, J., Su, Y., Cheng, C., Peng, J., Ma, J.: Equivariant point cloud analysis via learning orientations for message passing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18932–18941 (2022)
2022
Closest in time.
Zhemchuzhnikov, D., Igashov, I., Grudinin, S.: 6dcnn with roto-translational convolution filters for volumetric data processing. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 4707–4715 (2022)
2022
Closest in time.