N. Metropolis,et al. Equation of state calculations by fast computing machines , 1953 .
 Jorge Nocedal,et al. On the limited memory BFGS method for large scale optimization , 1989, Math. Program..
 W. Taylor,et al. Estimating polypeptideα-carbon distances from multiple sequence alignments , 1995 .
 T. Madden,et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.
 C Kooperberg,et al. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. , 1997, Journal of molecular biology.
 C Venclovas,et al. Processing and analysis of CASP3 protein structure predictions , 1999, Proteins.
 Stephen H. Bryant,et al. Domain size distributions can predict domain boundaries , 2000, Bioinform..
 Johannes Söding,et al. The HHpred interactive server for protein homology detection and structure prediction , 2005, Nucleic Acids Res..
 Jesper Ferkinghoff-Borg,et al. A generative, probabilistic model of local protein structure , 2008, Proceedings of the National Academy of Sciences.
 Feng Zhao,et al. Fragment-free approach to protein folding using conditional neural fields , 2010, Bioinform..
 C. Sander,et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families , 2011, Proceedings of the National Academy of Sciences.
 A. Biegert,et al. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment , 2012, Nature Methods.
 Jinbo Xu,et al. A position-specific distance-dependent statistical potential for protein structure and functional study. , 2012, Structure.
 E. Aurell,et al. Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
 Kam Y. J. Zhang,et al. Improving fragment quality for de novo structure prediction , 2014, Proteins.
 Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
 D. Baker,et al. Relaxation of backbone bond geometry improves protein energy landscape modeling , 2014, Protein science : a publication of the Protein Society.
 Matthew J. O’Meara,et al. Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta. , 2015, Journal of chemical theory and computation.
 David A. Lee,et al. CATH: an expanded resource to predict protein function through structure and sequence , 2017, Nucleic Acids Res..
 Zhen Li,et al. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model , 2017, PLoS Comput. Biol..
 Yang Zhang,et al. Template‐based and free modeling of I‐TASSER and QUARK pipelines using predicted contact maps in CASP12 , 2018, Proteins.
 Jinbo Xu,et al. Analysis of distance‐based protein structure prediction by deep learning in CASP13 , 2019, Proteins.
 End-to-End Differentiable Learning of Protein Structure. , 2019, Cell systems.
 David T. Jones,et al. Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints , 2019, Nature Communications.
 Demis Hassabis,et al. Improved protein structure prediction using potentials from deep learning , 2020, Nature.