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Peptides, short chains of amino acid residues, play a vital role in numerous biological processes by interacting with other target molecules, offering substantial potential in drug discovery.
A solution for the best rotation to relate two sets of vectors
Kabsch, W · 1976
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Logistic-normal distributions: Some properties and uses
Atchison, J. and Shen, S. M · 1980
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Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Kabsch, W. and Sander, C · 1983
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Structure and function of the signal peptide
Duffaud, G. D., Lehnhardt, S. K., March, P. E., and Inouye, M · 1985
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Peptide chemistry
Bodanszky, M · 1988
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Peptide secondary structure mimetics: recent advances and future challenges
Kahn, M · 1993
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Protein-peptide interactions
Stanfield, R. L. and Wilson, I. A · 1995
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Mini-review. application of combinatorial library methods in cancer research and drug discovery
Lam, K. S · 1997
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Lehninger principles of biochemistry, ; by david l. nelson and michael m. cox
Fisher, M · 2001
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On the role of the crystal environment in determining protein side-chain conformations
Jacobson, M. P., Friesner, R. A., Xiang, Z., and Honig, B · 2002
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Prediction of protein function from protein sequence and structure
Whisstock, J. C. and Lesk, A. M · 2003
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Functionalization and peptide-based delivery of magnetic nanoparticles as an intracellular mri contrast agent
Nitin, N., LaConte, L., Zurkiya, O., Hu, X., and Bao, G · 2004
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Tm-align: a protein structure alignment algorithm based on the tm-score
Zhang, Y. and Skolnick, J · 2005
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Peptide aptamers as guides for small-molecule drug discovery
Baines, I. C. and Colas, P · 2006
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Translating peptides into small molecules
Hummel, G., Reineke, U., and Reimer, U · 2006
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Helices and other secondary structures of β \beta -and γ \gamma -peptides
Seebach, D., Hook, D. F., and Glättli, A · 2006
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Peptide-mediated interactions in biological systems: new discoveries and applications
Petsalaki, E. and Russell, R. B · 2008
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Improved prediction of protein side-chain conformations with scwrl4
Krivov, G. G., Shapovalov, M. V., and Dunbrack Jr, R. L · 2009
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Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
Chaudhury, S., Lyskov, S., and Gray, J. J · 2010
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Secondary structure of cell-penetrating peptides controls membrane interaction and insertion
Eiríksdóttir, E., Konate, K., Langel, Ü., Divita, G., and Deshayes, S · 2010
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2010
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Synthetic therapeutic peptides: science and market
Vlieghe, P., Lisowski, V., Martinez, J., and Khrestchatisky, M · 2010
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New aspects of an old drug: metformin as a glucagon-like peptide 1 (glp-1) enhancer and sensitiser
Cho, Y. and Kieffer, T · 2011
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Structural and dynamic determinants of protein-peptide recognition
Dagliyan, O., Proctor, E. A., D’Auria, K. M., Ding, F., and Dokholyan, N. V · 2011
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Rosetta flexpepdock web server—high resolution modeling of peptide–protein interactions
London, N., Raveh, B., Cohen, E., Fathi, G., and Schueler-Furman, O · 2011
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Rosetta flexpepdock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors
Raveh, B., London, N., Zimmerman, L., and Schueler-Furman, O · 2011
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A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions
Shapovalov, M. V. and Dunbrack, R. L · 2011
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2011
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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Jantzen, R. T · 2012
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The future of peptide-based drugs
Craik, D. J., Fairlie, D. P., Liras, S., and Price, D · 2013
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Future directions for peptide therapeutics development
Kaspar, A. A. and Reichert, J. M · 2013
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The central glp-1: implications for food and drug reward
Skibicka, K. P · 2013
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Wang, W. and Carreira-Perpinán, M. A · 2013
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Peptide therapeutics: current status and future directions
Fosgerau, K. and Hoffmann, T · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Accurate de novo design of hyperstable constrained peptides
Bhardwaj, G., Mulligan, V. K., Bahl, C. D., Gilmore, J. M., Harvey, P. J., Cheneval, O., Buchko, G. W., Pulavarti, S. V., Kaas, Q., Eletsky, A., et al · 2016
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The coming of age of de novo protein design
Huang, P.-S., Boyken, S. E., and Baker, D · 2016
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The rosetta all-atom energy function for macromolecular modeling and design
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 · 2017
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Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M. and Söding, J · 2017
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Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
Cited alongside, same era.
The current state of peptide drug discovery: back to the future?
Henninot, A., Collins, J. C., and Nuss, J. M · 2018
Cited alongside, same era.
Introduction to Riemannian manifolds , volume 2
Lee, J. M · 2018
Cited alongside, same era.
Geometry of probability simplex via optimal transport
Li, W · 2018
Cited alongside, same era.
Generative models for graph-based protein design
Ingraham, J., Garg, V., Barzilay, R., and Jaakkola, T · 2019
Cited alongside, same era.
Peptide secondary structure prediction using evolutionary information
Singh, H., Singh, S., and Singh Raghava, G. P · 2019
Cited alongside, same era.
Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., and Ma, J · 2022
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An end-to-end deep learning method for rotamer-free protein side-chain packing
McPartlon, M. and Xu, J · 2022
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Dlpacker: deep learning for prediction of amino acid side chain conformations in proteins
Misiura, M., Shroff, R., Thyer, R., and Kolomeisky, A. B · 2022
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Pocket2mol: Efficient molecular sampling based on 3d protein pockets
Peng, X., Luo, S., Guan, J., Xie, Q., Peng, J., and Ma, J · 2022
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Proximal exploration for model-guided protein sequence design
Ren, Z., Li, J., Ding, F., Zhou, Y., Ma, J., and Peng, J · 2022
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Cited alongside, same era.
Pepbdb: a comprehensive structural database of biological peptide–protein interactions
Wen, Z., He, J., Tao, H., and Huang, S.-Y · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons
Jing, B., Eismann, S., Suriana, P., Townshend, R. J., and Dror, R · 2020
Cited alongside, same era.
Advances in medical imaging: aptamer-and peptide-targeted mri and ct contrast agents
Koudrina, A. and DeRosa, M. C · 2020
Cited alongside, same era.
Macromolecular modeling and design in rosetta: recent methods and frameworks
Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al · 2020
Cited alongside, same era.
Richemond, P. H., Dieleman, S., and Doucet, A · 2022
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Accelerating bayesian optimization for biological sequence design with denoising autoencoders
Stanton, S., Maddox, W., Gruver, N., Maffettone, P., Delaney, E., Greenside, P., and Wilson, A. G · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Trippe, B. L., Yim, J., Tischer, D., Baker, D., Broderick, T., Barzilay, R., and Jaakkola, T · 2022
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Language models generalize beyond natural proteins
Verkuil, R., Kabeli, O., Du, Y., Wicky, B. I., Milles, L. F., Dauparas, J., Baker, D., Ovchinnikov, S., Sercu, T., and Rives, A · 2022
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Therapeutic peptides: Current applications and future directions
Wang, L., Wang, N., Zhang, W., Cheng, X., Yan, Z., Shao, G., Wang, X., Wang, R., and Fu, C · 2022
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2022
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Protein structure generation via folding diffusion
Wu, K. E., Yang, K. K., Berg, R. v. d., Zou, J. Y., Lu, A. X., and Amini, A. P · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J · 2022
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Protein generation with evolutionary diffusion: sequence is all you need
Alamdari, S., Thakkar, N., van den Berg, R., Lu, A. X., Fusi, N., Amini, A. P., and Yang, K. K · 2023
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Improving de novo protein binder design with deep learning
Bennett, N. R., Coventry, B., Goreshnik, I., Huang, B., Allen, A., Vafeados, D., Peng, Y. P., Dauparas, J., Baek, M., Stewart, L., et al · 2023
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De novo generation and prioritization of target-binding peptide motifs from sequence alone
Bhat, S., Palepu, K., Yudistyra, V., Hong, L., Kavirayuni, V. S., Chen, T., Zhao, L., Wang, T., Vincoff, S., and Chatterjee, P · 2023
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Se (3)-stochastic flow matching for protein backbone generation
Bose, A. J., Akhound-Sadegh, T., Fatras, K., Huguet, G., Rector-Brooks, J., Liu, C.-H., Nica, A. C., Korablyov, M., Bronstein, M., and Tong, A · 2023
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Peptide binder design with inverse folding and protein structure prediction
Bryant, P. and Elofsson, A · 2023
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Riemannian flow matching on general geometries
Chen, R. T. and Lipman, Y · 2023
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Atomic context-conditioned protein sequence design using ligandmpnn
Dauparas, J., Lee, G. R., Pecoraro, R., An, L., Anishchenko, I., Glasscock, C., and Baker, D · 2023
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Diffusion on the probability simplex
Floto, G., Jonsson, T., Nica, M., Sanner, S., and Zhu, E. Z · 2023
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Protein discovery with discrete walk-jump sampling
Frey, N. C., Berenberg, D., Zadorozhny, K., Kleinhenz, J., Lafrance-Vanasse, J., Hotzel, I., Wu, Y., Ra, S., Bonneau, R., Cho, K., et al · 2023
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SSD-LM: Semi-autoregressive simplex-based diffusion language model for text generation and modular control
Han, X., Kumar, S., and Tsvetkov, Y · 2023
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Illuminating protein space with a programmable generative model
Ingraham, J. B., Baranov, M., Costello, Z., Barber, K. W., Wang, W., Ismail, A., Frappier, V., Lord, D. M., Ng-Thow-Hing, C., Van Vlack, E. R., et al · 2023
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Explainable deep hypergraph learning modeling the peptide secondary structure prediction
Jiang, Y., Wang, R., Feng, J., Jin, J., Liang, S., Li, Z., Yu, Y., Ma, A., Su, R., Zou, Q., et al · 2023
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End-to-end full-atom antibody design
Kong, X., Huang, W., and Liu, Y · 2023
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Generalized biomolecular modeling and design with rosettafold all-atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al · 2023
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Joint generation of protein sequence and structure with rosettafold sequence space diffusion
Lisanza, S. L., Gershon, J. M., Tipps, S. W. K., Arnoldt, L., Hendel, S., Sims, J. N., Li, X., and Baker, D · 2023
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Integrating protein structure prediction and bayesian optimization for peptide design
Manshour, N., He, F., Wang, D., and Xu, D · 2023
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Abdiffuser: Full-atom generation of in-vitro functioning antibodies
Martinkus, K., Ludwiczak, J., Cho, K., Lian, W.-C., Lafrance-Vanasse, J., Hotzel, I., Rajpal, A., Wu, Y., Bonneau, R., Gligorijevic, V., et al · 2023
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Progen2: exploring the boundaries of protein language models
Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., and Madani, A · 2023
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Single-chain dimers from de novo immunoglobulins as robust scaffolds for multiple binding loops
Roel-Touris, J., Nadal, M., and Marcos, E · 2023
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Equivariant flow matching with hybrid probability transport
Song, Y., Gong, J., Xu, M., Cao, Z., Lan, Y., Ermon, S., Zhou, H., and Ma, W.-Y · 2023
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Helixgan a deep-learning methodology for conditional de novo design of α \alpha -helix structures
Xie, X., Valiente, P. A., and Kim, P. M · 2023
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De novo design of luciferases using deep learning
Yeh, A. H.-W., Norn, C., Kipnis, Y., Tischer, D., Pellock, S. J., Evans, D., Ma, P., Lee, G. R., Zhang, J. Z., Anishchenko, I., et al · 2023
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Protein design with guided discrete diffusion
Gruver, N., Stanton, S., Frey, N., Rudner, T. G., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A. G · 2024
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Q-biolip: A comprehensive resource for quaternary structure-based protein–ligand interactions
Wei, H., Wang, W., Peng, Z., and Yang, J · 2024
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Graph denoising diffusion for inverse protein folding
Yi, K., Zhou, B., Shen, Y., Liò, P., and Wang, Y · 2024
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Improved motif-scaffolding with se (3) flow matching
Yim, J., Campbell, A., Mathieu, E., Foong, A. Y., Gastegger, M., Jiménez-Luna, J., Lewis, S., Satorras, V. G., Veeling, B. S., Noé, F., et al · 2024
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