Fetching the paper…
Reading the bibliography…
The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding.
Relational graph attention networks
Busbridge, D., Sherburn, D., Cavallo, P., and Hammerla, N. Y. (2019) · 1904
Earlier work this paper cites.
Liu, X., He, P., Chen, W., and Gao, J. (2019b) · 1904
Earlier work this paper cites.
SpanBERT: Improving pre-training by representing and predicting spans
Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., and Levy, O. (2019) · 1907
Earlier work this paper cites.
Finbert: Financial sentiment analysis with pre-trained language models
Araci, D. (2019) · 1908
Earlier work this paper cites.
Automatically extracting challenge sets for non local phenomena in neural machine translation
Choshen, L. and Abend, O. (2019) · 1909
Earlier work this paper cites.
The arrangement of amino acids in proteins
Sanger, F. (1952) · 1952
Earlier work this paper cites.
Experimental and theoretical aspects of protein folding
Cb, A. and Scheraga, H. A. (1975) · 1975
Earlier work this paper cites.
Maximum likelihood alignment of dna sequences
Bishop, M. and Thompson, E. A. (1986) · 1986
Earlier work this paper cites.
Guide to biochemistry
Blackstock, J. C. (1989) · 1989
Earlier work this paper cites.
Protein secondary structure prediction with a neural network
Holley, L. H. and Karplus, M. (1989) · 1989
Earlier work this paper cites.
Untersuchungen zu dynamischen neuronalen netzen
Hochreiter, S. (1991) · 1991
Earlier work this paper cites.
Protein folding funnels: a kinetic approach to the sequence-structure relationship
Leopold, P. E., Montal, M., and Onuchic, J. N. (1992) · 1992
Earlier work this paper cites.
Improved prediction of protein secondary structure by use of sequence profiles and neural networks
Rost, B. and Sander, C. (1993) · 1993
Earlier work this paper cites.
Funnels, pathways, and the energy landscape of protein folding: a synthesis
Bryngelson, J. D., Onuchic, J. N., Socci, N. D., and Wolynes, P. G. (1995) · 1995
Earlier work this paper cites.
Scop: a structural classification of proteins database
Hubbard, T., Ailey, B., Brenner, S. E., Murzin, A. G., and Chothia, C. (1997) · 1997
Earlier work this paper cites.
Cath – a hierarchic classification of protein domain structures
Orengo, C. A., Michie, A., Jones, S., Jones, D. T., Swindells, M. B., and Thornton, J. M. (1997) · 1997
Earlier work this paper cites.
Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions
Simons, K. T., Kooperberg, C., Huang, E. S., and Baker, D. (1997) · 1997
Earlier work this paper cites.
Learning to learn: introduction and overview
Thrun, S. and Pratt, L. (1998) · 1998
Earlier work this paper cites.
Gene ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S. E., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M., and Sherlock, G. (2000) · 2000
Earlier work this paper cites.
The protein data bank
Berman, H. M., Westbrook, J. D., Feng, Z., Gilliland, G. L., Bhat, T. N., Weissig, H., Shindyalov, I. N., and Bourne, P. E. (2000) · 2000
Earlier work this paper cites.
Predicting protein quaternary structure by pseudo amino acid composition
Chou, K.-C. and Cai, Y.-D. (2003) · 2003
Earlier work this paper cites.
Touchstone ii: a new approach to ab initio protein structure prediction
Zhang, Y., Kolinski, A., and Skolnick, J. (2003) · 2003
Earlier work this paper cites.
A threading approach to protein structure prediction: studies on tnf-like molecules, rev proteins, and protein kinases
Ihm, Y. (2004) · 2004
Earlier work this paper cites.
Protein structure prediction using rosetta
Rohl, C. A., Strauss, C. E. M., Misura, K. M., and Baker, D. (2004) · 2004
Earlier work this paper cites.
Orthologs, paralogs, and evolutionary genomics
Koonin, E. V. (2005) · 2005
Earlier work this paper cites.
Evolino: Hybrid neuroevolution/optimal linear search for sequence prediction
Schmidhuber, J., Wierstra, D., and Gomez, F. J. (2005) · 2005
Earlier work this paper cites.
Pisces: recent improvements to a pdb sequence culling server
Wang, G. and Dunbrack, R. L. (2005) · 2005
Earlier work this paper cites.
Tm-align: a protein structure alignment algorithm based on the tm-score
Zhang, Y. and Skolnick, J. (2005) · 2005
Earlier work this paper cites.
Protein structure determination from nmr chemical shifts
Cavalli, A., Salvatella, X., Dobson, C. M., and Vendruscolo, M. (2007) · 2007
Earlier work this paper cites.
Fast model-based protein homology detection without alignment
Hochreiter, S., Heusel, M., and Obermayer, K. (2007) · 2007
Earlier work this paper cites.
The application of hidden markov models in speech recognition
Gales, M., Young, S., et al. (2008) · 2008
Earlier work this paper cites.
A dynamic bayesian network approach to protein secondary structure prediction
Yao, X.-Q., Zhu, H., and She, Z.-S. (2008) · 2008
Earlier work this paper cites.
A novel connectionist system for improved unconstrained handwriting recognition
AGMLS, F., Bunke, R., and Schmiduber, J. (2009) · 2009
Earlier work this paper cites.
Identification of direct residue contacts in protein-protein interaction by message passing
Weigt, M., White, R. A., Szurmant, H., Hoch, J. A., and Hwa, T. (2009) · 2009
Earlier work this paper cites.
Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
Chaudhury, S., Lyskov, S., and Gray, J. J. (2010) · 2010
Earlier work this paper cites.
A survey on transfer learning
Pan, S. J. and Yang, Q. (2010) · 2010
Earlier work this paper cites.
Scaling hidden markov language models
Chiu, J. T. and Rush, A. M. (2020) · 2011
Earlier work this paper cites.
The complex folding network of single calmodulin molecules
Stigler, J., Ziegler, F., Gieseke, A., Gebhardt, J. C. M., and Rief, M. (2011) · 2011
Earlier work this paper cites.
De novo sequencing and homology searching
Ma, B. and Johnson, R. (2012) · 2012
Earlier work this paper cites.
Protein structure determination from pseudocontact shifts using rosetta
Schmitz, C., Vernon, R. B., Otting, G., Baker, D., and Huber, T. (2012) · 2012
Earlier work this paper cites.
A probabilistic fragment-based protein structure prediction algorithm
Simoncini, D., Berenger, F., Shrestha, R., and Zhang, K. Y. J. (2012) · 2012
Earlier work this paper cites.
Cross-Modal Learning
Skocaj, D., Leonardis, A., and Kruijff, G.-J. M. (2012) · 2012
Earlier work this paper cites.
Genomics-aided structure prediction
Sulkowska, J. I., Morcos, F., Weigt, M., Hwa, T., and Onuchic, J. N. (2012) · 2012
Earlier work this paper cites.
Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field
Xu, D. and Zhang, Y. (2012) · 2012
Earlier work this paper cites.
Semantic parsing as machine translation
Andreas, J., Vlachos, A., and Clark, S. (2013) · 2013
Earlier work this paper cites.
Update on activities at the universal protein resource (uniprot) in 2013
Consortium, U. (2013) · 2013
Earlier work this paper cites.
First links in the markov chain
Hayes, B. et al. (2013) · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G. S., and Dean, J. (2013) · 2013
Earlier work this paper cites.
Solving ion channel kinetics with the qub software
Nicolai, C. and Sachs, F. (2013) · 2013
Earlier work this paper cites.
Protein secondary structure prediction using support vector machines (svms)
Patel, M. and Shah, H. B. (2013) · 2013
Earlier work this paper cites.
Dropout improves recurrent neural networks for handwriting recognition
Pham, V., Bluche, T., Kermorvant, C., and Louradour, J. (2013) · 2013
Earlier work this paper cites.
Dna motif elucidation using belief propagation
Wong, K.-C., Chan, T.-M., Peng, C., Li, Y., and Zhang, Z. (2013) · 2013
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
De novo structure prediction of globular proteins aided by sequence variation-derived contacts
Kosciolek, T. and Jones, D. T. (2014) · 2014
Earlier work this paper cites.
Sspro/accpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity
Magnan, C. and Baldi, P. (2014) · 2014
Earlier work this paper cites.
Practical aspects of protein co-evolution
Ochoa, D. and Pazos, F. (2014) · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M. S., Berg, A. C., and Fei-Fei, L. (2014) · 2014
Earlier work this paper cites.
Protein secondary structure prediction with long short term memory networks
Sønderby, S. K. and Winther, O. (2014) · 2014
Earlier work this paper cites.
Deep supervised and convolutional generative stochastic network for protein secondary structure prediction
Zhou, J. and Troyanskaya, O. G. (2014) · 2014
Earlier work this paper cites.
Confold: Residue-residue contact-guided ab initio protein folding
Adhikari, B., Bhattacharya, D., Cao, R., and Cheng, J. (2015) · 2015
Earlier work this paper cites.
Continuous distributed representation of biological sequences for deep proteomics and genomics
Asgari, E. and Mofrad, M. R. K. (2015) · 2015
Earlier work this paper cites.
Combining evolutionary information and an iterative sampling strategy for accurate protein structure prediction
Braun, T., Leman, J. K., and Lange, O. F. (2015) · 2015
Earlier work this paper cites.
The master algorithm: How the quest for the ultimate learning machine will remake our world
Domingos, P. (2015) · 2015
Earlier work this paper cites.
Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning
Heffernan, R., Paliwal, K. K., Lyons, J., Dehzangi, A., Sharma, A., Wang, J., Sattar, A., Yang, Y., and Zhou, Y. (2015) · 2015
Earlier work this paper cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. E. (2015) · 2015
Earlier work this paper cites.
Novor: real-time peptide de novo sequencing software
Ma, B. (2015) · 2015
Earlier work this paper cites.
Rbo aleph: leveraging novel information sources for protein structure prediction
Mabrouk, M., Putz, I., Werner, T., Schneider, M., Neeb, M., Bartels, P., and Brock, O. (2015) · 2015
Earlier work this paper cites.
A deep learning network approach to ab initio protein secondary structure prediction
Spencer, M., Eickholt, J., and Cheng, J. (2015) · 2015
Earlier work this paper cites.
Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., and Wu, C. H. (2015) · 2015
Earlier work this paper cites.
Protein secondary structure prediction using deep convolutional neural fields
Wang, S., Peng, J., Ma, J., and Xu, J. (2015) · 2015
Earlier work this paper cites.
Unicon3d: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling
Bhattacharya, D., Cao, R., and Cheng, J. (2016) · 2016
Earlier work this paper cites.
Protein secondary structure prediction using deep multi-scale convolutional neural networks and next-step conditioning
Busia, A., Collins, J., and Jaitly, N. (2016) · 2016
Earlier work this paper cites.
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Earlier work this paper cites.
Multiplicative lstm for sequence modelling
Krause, B., Lu, L., Murray, I., and Renals, S. (2016) · 2016
Earlier work this paper cites.
Neural architectures for named entity recognition
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C. (2016) · 2016
Earlier work this paper cites.
Generative topic embedding: a continuous representation of documents
Li, S., Chua, T.-S., Zhu, J., and Miao, C. (2016) · 2016
Earlier work this paper cites.
Must-cnn: a multilayer shift-and-stitch deep convolutional architecture for sequence-based protein structure prediction
Lin, Z., Lanchantin, J., and Qi, Y. (2016) · 2016
Earlier work this paper cites.
Improved de novo structure prediction in casp11 by incorporating coevolution information into rosetta
Ovchinnikov, S., Kim, D. E., Wang, R. Y.-R., Liu, Y., DiMaio, F., and Baker, D. (2016) · 2016
Earlier work this paper cites.
A survey of transfer learning
Weiss, K. R., Khoshgoftaar, T. M., and Wang, D. (2016) · 2016
Earlier work this paper cites.
Dncon2: Improved protein contact prediction using two-level deep convolutional neural networks
Adhikari, B., Hou, J., and Cheng, J. (2017) · 2017
Earlier work this paper cites.
High gc content causes orphan proteins to be intrinsically disordered
Basile, W., Sachenkova, O., Light, S., and Elofsson, A. (2017) · 2017
Earlier work this paper cites.
Scope: Manual curation and artifact removal in the structural classification of proteins - extended database
Chandonia, J.-M., Fox, N. K., and Brenner, S. E. (2017) · 2017
Earlier work this paper cites.
Protein contact maps: A binary depiction of protein 3d structures
Emerson, I. A. and Amala, A. (2017) · 2017
Earlier work this paper cites.
Conformational space sampling method using multi-subpopulation differential evolution for de novo protein structure prediction
Hao, X.-H., Zhang, G.-J., and Zhou, X.-G. (2017) · 2017
Earlier work this paper cites.
A hybrid method for prediction of protein secondary structure based on multiple artificial neural networks
Hasic, H., Buza, E., and Akagic, A. (2017) · 2017
Cited alongside, same era.
Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility
Heffernan, R., Yang, Y., Paliwal, K. K., and Zhou, Y. (2017) · 2017
Cited alongside, same era.
A structured self-attentive sentence embedding
Lin, Z., Feng, M., Santos, C. N. d., Yu, M., Xiang, B., Zhou, B., and Bengio, Y. (2017) · 2017
Cited alongside, same era.
Enhancing evolutionary couplings with deep convolutional neural networks
Liu, Y., Palmedo, P., Ye, Q., Berger, B., and Peng, J. (2017) · 2017
Cited alongside, same era.
Uniclust databases of clustered and deeply annotated protein sequences and alignments
Deepdist: real-value inter-residue distance prediction with deep residual convolutional network
Wu, T., Guo, Z., Hou, J., and Cheng, J. (2020) · 2020
Later among the works it cites.
Textbrewer: An open-source knowledge distillation toolkit for natural language processing
Yang, Z., Cui, Y., Chen, Z., Che, W., Liu, T., Wang, S., and Hu, G. (2020) · 2020
Later among the works it cites.
Template-based prediction of protein structure with deep learning
Zhang, H. and Shen, Y. (2020) · 2020
Later among the works it cites.
Accurate prediction of protein structures and interactions using a three-track neural network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., Millán, C., Park, H., Adams, C., Glassman, C. R., DeGiovanni, A., Pereira, J. H., Rodrigues, A. V., van Dijk, A. A., Ebrecht, A. C., Opperman, D. J., Sagmeister, T., Buhlheller, C., Pavkov-Keller, T., Rathinaswamy, M. K., Dalwadi, U., Yip, C. K., Burke, J. E., Garcia, K. C., Grishin, N. V., Adams, P. D., Read, R. J., and Baker, D. (2021) · 2021
Later among the works it cites.
Beit: Bert pre-training of image transformers
Bao, H., Dong, L., and Wei, F. (2021) · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mirdita, M., von den Driesch, L., Galiez, C., Martin, M. J., Söding, J., and Steinegger, M. (2017) · 2017
Cited alongside, same era.
Learning to generate reviews and discovering sentiment
Radford, A., Jozefowicz, R., and Sutskever, I. (2017) · 2017
Cited alongside, same era.
Balancing exploration and exploitation in population-based sampling improves fragment-based de novo protein structure prediction
Simoncini, D., Schiex, T., and Zhang, K. Y. J. (2017) · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
Cited alongside, same era.
Bayesian statistical approach for protein residue-residue contact prediction
Vorberg, S. (2017) · 2017
Cited alongside, same era.
Recent trends in deep learning based natural language processing
Young, T., Hazarika, D., Poria, S., and Cambria, E. (2017) · 2017
Cited alongside, same era.
End-to-end differentiable learning of protein structure
AlQuraishi, M. (2018) · 2018
Cited alongside, same era.
Generative modeling for protein structures
Anand, N. and Huang, P.-S. (2018) · 2018
Cited alongside, same era.
Later among the works it cites.
Learning the protein language: Evolution, structure, and function
Bepler, T. and Berger, B. (2021) · 2021
Later among the works it cites.
Single layers of attention suffice to predict protein contacts
Bhattacharya, N., Thomas, N., Rao, R., Daupras, J., Koo, P. K., Baker, D., Song, Y. S., and Ovchinnikov, S. (2021) · 2021
Later among the works it cites.
Colossal-ai: A unified deep learning system for large-scale parallel training
Bian, Z., Liu, H., Wang, B., Huang, H., Li, Y., Wang, C., Cui, F., and You, Y. (2021) · 2021
Later among the works it cites.
Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models
Bond-Taylor, S., Leach, A., Long, Y., and Willcocks, C. G. (2021) · 2021
Later among the works it cites.
Improved prediction of protein-protein interactions using alphafold2 and extended multiple-sequence alignments
Bryant, P., Pozzati, G., and Elofsson, A. (2021) · 2021
Later among the works it cites.
Fold2seq: A joint sequence(1d)-fold(3d) embedding-based generative model for protein design
Cao, Y., Das, P., Chen, P.-Y., Chenthamarakshan, V., Melnyk, I., and Shen, Y. (2021) · 2021
Later among the works it cites.
Distillation of msa embeddings to folded protein structures with graph transformers
Costa, A., Ponnapati, M., Jacobson, J. M., and Chatterjee, P. (2021) · 2021
Later among the works it cites.
Identification of enzymatic active sites with unsupervised language modeling
Dassi, L. K., Manica, M., Probst, D., Schwaller, P., Teukam, Y. G. N., and Laino, T. (2021) · 2021
Later among the works it cites.
Prottrans: Towards cracking the language of lifes code through self-supervised deep learning and high performance computing
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Yu, W., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., Bhowmik, D., and Rost, B. (2021) · 2021
Later among the works it cites.
Protein complex prediction with alphafold-multimer
Evans, R., O’Neill, M. J., Pritzel, A., Antropova, N., Senior, A. W., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., Ronneberger, O., Bodenstein, S., Zielinski, M., Bridgland, A., Potapenko, A., Cowie, A., Tunyasuvunakool, K., Jain, R. D., Clancy, E., Kohli, P., Jumper, J. M., and Hassabis, D. (2021) · 2021
Later among the works it cites.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
Fedus, W., Zoph, B., and Shazeer, N. (2021) · 2021
Later among the works it cites.
Pre-trained models: Past, present and future
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Zhang, L., Han, W., Huang, M., Jin, Q., Lan, Y., Liu, Y., Liu, Z., Lu, Z., Qiu, X., Song, R., Tang, J., Wen, J.-R., Yuan, J., Zhao, W. X., and Zhu, J. (2021) · 2021
Later among the works it cites.
Alphafill: enriching the alphafold models with ligands and co-factors
Hekkelman, M. L., d. de Vries, I., Joosten, R. P., and Perrakis, A. (2021) · 2021
Later among the works it cites.
Evolutionary velocity with protein language models
Hie, B., Yang, K. K., and Kim, P. S. (2021) · 2021
Later among the works it cites.
Bidirectional language modeling: A systematic literature review
Jahan, M. S., Khan, H. U., Akbar, S., Farooq, M. U., Gul, S., and Amjad, A. (2021) · 2021
Later among the works it cites.
Fast and effective protein model refinement using deep graph neural networks
Jing, X. and Xu, J. (2021) · 2021
Later among the works it cites.
Highly accurate protein structure prediction with alphafold
Jumper, J. M., Evans, R. O., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R. D., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D. L., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P., and Hassabis, D. (2021) · 2021
Later among the works it cites.
Study of real-valued distance prediction for protein structure prediction with deep learning
Li, J. and Xu, J. (2021) · 2021
Later among the works it cites.
Short-term traffic flow prediction for urban road sections based on time series analysis and lstm_bilstm method
Ma, C., Dai, G., and Zhou, J. (2021) · 2021
Later among the works it cites.
Deep neural language modeling enables functional protein generation across families
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., Fraser, J. S., and Naik, N. (2021) · 2021
Later among the works it cites.
Toward more general embeddings for protein design: Harnessing joint representations of sequence and structure
Mansoor, S., Baek, M., Madan, U., and Horvitz, E. (2021) · 2021
Later among the works it cites.
Adversarial contrastive pre-training for protein sequences
McDermott, M. B. A., Yap, B., Hsu, T.-M. H., Jin, D., and Szolovits, P. (2021) · 2021
Later among the works it cites.
Language models enable zero-shot prediction of the effects of mutations on protein function
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., and Rives, A. (2021) · 2021
Later among the works it cites.
Using alphafold to predict the impact of single mutations on protein stability and function
Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., and Ivankov, D. N. (2021) · 2021
Later among the works it cites.
Crash: Raw audio score-based generative modeling for controllable high-resolution drum sound synthesis
Rouard, S. and Hadjeres, G. (2021) · 2021
Later among the works it cites.
A review of protein structure prediction using deep learning
Susanty, M., Rajab, T. E., and Hertadi, R. (2021) · 2021
Later among the works it cites.
Prediction of rna-protein interactions using a nucleotide language model
Yamada, K. and Hamada, M. (2021) · 2021
Later among the works it cites.
Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes
Yan, Y. and Huang, S.-Y. (2021) · 2021
Later among the works it cites.
Multi-task deep learning for concurrent prediction of protein structural properties
Zhang, B., Li, J., Quan, L., and Lyu, Q. (2021) · 2021
Later among the works it cites.
Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization
Ahdritz, G., Bouatta, N., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., Nowaczynski, A., Wang, B., Stepniewska-Dziubinska, M. M., Zhang, S., Ojewole, A., Guney, M. E., Biderman, S., Watkins, A. M., Ra, S., Lorenzo, P. R., Nivon, L., Weitzner, B., Ban, Y.-E. A., Sorger, P. K., Mostaque, E., Zhang, Z., Bonneau, R., and AlQuraishi, M. (2022) · 2022
Closest in time.
A structural biology community assessment of alphafold2 applications
Akdel, M., Pires, D. E., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · 2022
Closest in time.
Accurate prediction of nucleic acid and protein-nucleic acid complexes using rosettafoldna
Baek, M., Mchugh, R., Anishchenko, I., Baker, D., and Dimaio, F. (2022) · 2022
Closest in time.
A neural probabilistic language model
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., Kandola, J., Hofmann, T., Poggio, T., and Shawe-Taylor, J. (2022) · 2022
Closest in time.
Learning the protein language: Evolution, structure, and function
Bepler, T. and Berger, B. (2022) · 2022
Closest in time.
Few shot protein generation
Bepler, T. and Ram, S. (2022) · 2022
Closest in time.
Can alphafold2 predict the impact of missense mutations on structure?
Buel, G. R. and Walters, K. J. (2022) · 2022
Closest in time.
Alphafold encodes the principles to identify high affinity peptide binders
Chang, L. and Perez, A. (2022) · 2022
Closest in time.
Fastfold: Reducing alphafold training time from 11 days to 67 hours
Cheng, S., Wu, R., Yu, Z., Li, B., Zhang, X., Peng, J., and You, Y. (2022) · 2022
Closest in time.
Single-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., and AlQuraishi, M. (2022) · 2022
Closest in time.
Robust deep learning based protein sequence design using proteinmpnn
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., Haas, R. J. D., Bethel, N., Leung, P. J. Y., Huddy, T. F., Pellock, S., Tischer, D., Chan, F., Koepnick, B., Nguyen, H., Kang, A., Sankaran, B., Bera, A. K., King, N. P., and Baker, D. (2022) · 2022
Closest in time.
Petribert : Augmenting bert with tridimensional encoding for inverse protein folding and design
Dumortier, B., Liutkus, A., Mas, A., and Krouk, G. (2022) · 2022
Closest in time.
Prottrans: Towards cracking the language of life’s code through self-supervised deep learning and high performance computing
Elnaggar, A., Heinzinger, M., Dallago, C., Rihawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., Bhowmik, D., and Rost, B. (2022) · 2022
Closest in time.
Predicting the structure of large protein complexes using alphafold and monte carlo tree search
Elofsson, A., Bryant, P., Pozzati, G., Zhu, W., Shenoy, A., and Kundrotas, P. (2022) · 2022
Closest in time.
Helixfold-single: Msa-free protein structure prediction by using protein language model as an alternative
Fang, X., Wang, F., Liu, L., He, J., Lin, D., Xiang, Y., Zhang, X., Wu, H., Li, H., and Song, L. (2022) · 2022
Closest in time.
Controllable protein design with language models
Ferruz, N. and Höcker, B. (2022) · 2022
Closest in time.
A deep unsupervised language model for protein design
Ferruz, N., Schmidt, S., and Höcker, B. (2022) · 2022
Closest in time.
Pre-training co-evolutionary protein representation via a pairwise masked language model
He, L., Zhang, S., Wu, L., Xia, H., Ju, F., Zhang, H., Liu, S., Xia, Y., Zhu, J., Deng, P., Shao, B., Qin, T., and Liu, T.-Y. (2022) · 2022
Closest in time.
Contrastive learning on protein embeddings enlightens midnight zone
Heinzinger, M., Littmann, M., Sillitoe, I., Bordin, N., Orengo, C., and Rost, B. (2022) · 2022
Closest in time.
Rita: a study on scaling up generative protein sequence models
Hesslow, D., Zanichelli, N., Notin, P., Poli, I., and Marks, D. (2022) · 2022
Closest in time.
Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., De, D., Casas, L., Hendricks, L., Welbl, J., Clark, A., Hennigan, T., Noland, E., Millican, K., Driessche, G. V. D., Damoc, B., Guy, A., Osindero, S., Simonyan, K., Elsen, E., Rae, J., Vinyals, O., and Sifre, L. (2022) · 2022
Closest in time.
Netsurfp-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning
Høie, M. H., Kiehl, E. N., Petersen, B., Nielsen, M., Winther, O., Nielsen, H., Hallgren, J., and Marcatili, P. (2022) · 2022
Closest in time.
Using metagenomic data to boost protein structure prediction and discovery
Hou, Q., Pucci, F., Pan, F., Xue, F., Rooman, M., and Feng, Q. (2022) · 2022
Closest in time.
Learning inverse folding from millions of predicted structures
Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A., and Rives, A. (2022) · 2022
Closest in time.
Exploring evolution-based & -free protein language models as protein function predictors
Hu, M., Yuan, F., Yang, K. K., Ju, F., Su, J., Wang, H., Yang, F., and Ding, Q. (2022) · 2022
Closest in time.
Transformer quality in linear time
Hua, W., Dai, Z., Liu, H., and Le, Q. V. (2022) · 2022
Closest in time.
Equifold: Protein structure prediction with a novel coarse-grained structure representation
Lee, J. H., Yadollahpour, P., Watkins, A., Frey, N. C., Leaver-Fay, A., Ra, S., Cho, K., Gligorijevic, V., Regev, A., Bonneau, R., Design, P., and Genentech (2022) · 2022
Closest in time.
Language models: past, present, and future
Li, H. (2022) · 2022
Closest in time.
Uni-fold: An open-source platform for developing protein folding models beyond alphafold
Li, Z., Liu, X., Chen, W., Shen, F., Bi, H., Ke, G., and Zhang, L. (2022) · 2022
Closest in time.
Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Liao, Y.-L. and Smidt, T. (2022) · 2022
Closest in time.
Language models of protein sequences at the scale of evolution enable accurate structure prediction
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Costa, A. D. S., Fazel-Zarandi, M., Sercu, T., Candido, S., and Rives, A. (2022) · 2022
Closest in time.
Colabfold: making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., and Steinegger, M. (2022) · 2022
Closest in time.
Progen2: Exploring the boundaries of protein language models
Nijkamp, E., Ruffolo, J., Weinstein, E. N., Naik, N., Madani, A., and Research, S. (2022) · 2022
Closest in time.
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D. S., and Gal, Y. (2022) · 2022
Closest in time.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2022) · 2022
Closest in time.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022) · 2022
Closest in time.
Evidence for and Applications of Physics-Based Reasoning in AlphaFold
Roney, J. (2022) · 2022
Closest in time.
State-of-the-art estimation of protein model accuracy using alphafold
Roney, J. P. and Ovchinnikov, S. (2022) · 2022
Closest in time.
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
Ruffolo, J. A. and Gray, J. J. (2022) · 2022
Closest in time.
E2efold-3d: End-to-end deep learning method for accurate de novo rna 3d structure prediction
Shen, T., Hu, Z., Peng, Z., Chen, J., Xiong, P., Hong, L., Zheng, L., Wang, Y., King, I., Wang, S., Sun, S., and Li, Y. (2022) · 2022
Closest in time.
Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model
Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., Korthikanti, V., Zhang, E., Child, R., Aminabadi, R. Y., Bernauer, J., Song, X., Shoeybi, M., He, Y., Houston, M., Tiwary, S., and Catanzaro, B. (2022) · 2022
Closest in time.
Speach_af: Sampling protein ensembles and conformational heterogeneity with alphafold2
Stein, R. A. and Mchaourab, H. S. (2022) · 2022
Closest in time.
Generative de novo protein design with global context
Tan, C., Gao, Z., Xia, J., and Li, S. Z. (2022) · 2022
Closest in time.
Learning functional properties of proteins with language models
Unsal, S., Atas, H., Albayrak, M., Turhan, K., Acar, A. C., and Doğan, T. (2022) · 2022
Closest in time.
Advancing protein language models with linguistics: a roadmap for improved interpretability
Vu, M. H., Akbar, R., Robert, P. A., Swiatczak, B., Sandve, G. K., Greiff, V., Trygve, D., and Haug, T. (2022) · 2022
Closest in time.
Atomic protein structure refinement using all-atom graph representations and se(3)-equivariant graph neural networks
Wu, T. and Chen, C. (2022) · 2022
Closest in time.
Simgrace: A simple framework for graph contrastive learning without data augmentation
Xia, J., Wu, L., Chen, J., Hu, B., and Li, S. Z. (2022a) · 2022
Closest in time.
Pre-training graph neural networks for molecular representations: Retrospect and prospect
Xia, J., Zhu, Y., Du, Y., and Li, S. Z. (2022b) · 2022
Closest in time.
Peer: A comprehensive and multi-task benchmark for protein sequence understanding
Xu, M., Zhang, Z., Lu, J., Zhu, Z., Zhang, Y., Ma, C., Liu, R., and Tang, J. (2022) · 2022
Closest in time.