参考资料
[1]Ferruz et al. From sequence to function through structure: Deep learning for protein design. Comput Struct Biotech. 2022
[2]Senior et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020
[3]Jumper et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021
[4]Li et al. Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles. Protein. 2014
[5]Chen et al. To Improve protein sequence profile prediction through image captioning on pairwise residue distance map. J Chem Inf Model. 2020
[6]Qi et al. DenseCPD: improving the accuracy of neural-network-based computational protein sequence design with DenseNet. J Chem Inf Model. 2020
[7]Ingraham et al. Generative models for graph-based protein design. NeurIPS. 2019
[8]Liu et al. Rotamer-free protein sequence design based on deep learning and self-consistency. Nat Comput Sci. 2022
[9]Anand et al. Generative modeling for protein structures. NeurIPS. 2018
[10]Alford et al. The Rosetta all-atom energy function for macromolecular modeling and design. J Chem Theory Comput. 2017
[11]Eguchi et al. Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation. PLOS Comput Biol. 2022
[12]Huang et al. A backbone-centred energy function of neural networks for protein design. Nature. 2022
[13]Rives et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc Natl Acad Sci. 2021
[14]Elnaggar et al. ProtTrans: Towards cracking the language of lifes code through self-supervised deep learning and high performance computing. IEEE Trans Pat Anal Mach Intel. 2021
[15]Ferruz et al. ProtGPT2 is a deep unsupervised language model for protein design. Nat Common. 2022
[16]Notin et al. Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. ICML. 2022
[17]Frazer et al. Disease variant prediction with deep generative models of evolutionary data. Nature. 2021
[18]Castro et al. Transformer-based protein generation with regularized latent space optimization. Nature. 2022
[19]Wang et al. Scaffolding protein functional sites using deep learning. Science. 2022
[20]Baek et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021
[21]Anishchenko et al. De novo protein design by deep network hallucination。Nature. 2021