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Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization.
Conformation of polypeptides and proteins
Ramachandran, G. N. & Sasisekharan, V · 1968
Earlier work this paper cites.
An internal-coordinate monte carlo method for searching conformational space
Chang, G., Guida, W. C. & Still, W. C · 1989
Earlier work this paper cites.
Merck molecular force field. v. extension of MMFF94 using experimental data, additional computational data, and empirical rules
Halgren, T. A · 1996
Earlier work this paper cites.
Low mode search. an efficient, automated computational method for conformational analysis: Application to cyclic and acyclic alkanes and cyclic peptides
Kolossváry, I. & Guida, W. C · 1996
Earlier work this paper cites.
Low-mode conformational search elucidated: Application to C39H80 and flexible docking of 9-deazaguanine inhibitors into PNP
Kolossváry, I. & Guida, W. C · 1999
Earlier work this paper cites.
The protein data bank
Berman, H. M. et al · 2000
Earlier work this paper cites.
RDKit: Open-source cheminformatics (2006)
Landrum, G · 2006
Earlier work this paper cites.
The exploration of macrocycles for drug discovery–an underexploited structural class
Driggers, E. M., Hale, S. P., Lee, J. & Terrett, N. K · 2008
Earlier work this paper cites.
Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and cambridge structural database
Hawkins, P. C. D., Skillman, A. G., Warren, G. L., Ellingson, B. A. & Stahl, M. T · 2010
Earlier work this paper cites.
Conformer generation with OMEGA: learning from the data set and the analysis of failures
Hawkins, P. C. D. & Nicholls, A · 2012
Earlier work this paper cites.
Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17
Ruddigkeit, L., van Deursen, R., Blum, L. C. & Reymond, J.-L · 2012
Earlier work this paper cites.
How proteins bind macrocycles
Villar, E. A. et al · 2014
Earlier work this paper cites.
Macrocycle conformational sampling with MacroModel
Watts, K. S., Dalal, P., Tebben, A. J., Cheney, D. L. & Shelley, J. C · 2014
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M. & von Lilienfeld, O. A · 2014
Earlier work this paper cites.
Better informed distance geometry: Using what we know to improve conformation generation
Riniker, S. & Landrum, G. A · 2015
Earlier work this paper cites.
Quantifying the chameleonic properties of macrocycles and other high-molecular-weight drugs
Whitty, A. et al · 2016
Earlier work this paper cites.
The cambridge structural database
Groom, C. R., Bruno, I. J., Lightfoot, M. P. & Ward, S. C · 2016
Earlier work this paper cites.
Improving accuracy, diversity, and speed with prime macrocycle conformational sampling
Sindhikara, D. et al · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E · 2017
Earlier work this paper cites.
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O. & Roitberg, A. E · 2017
Cited alongside, same era.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K. et al · 2017
Cited alongside, same era.
RNA display methods for the discovery of bioactive macrocycles
Huang, Y., Wiedmann, M. M. & Suga, H · 2019
Cited alongside, same era.
Macrocyclic peptides as drug candidates: Recent progress and remaining challenges
Vinogradov, A. A., Yin, Y. & Suga, H · 2019
Cited alongside, same era.
Molecular geometry prediction using a deep generative graph neural network
Mansimov, E., Mahmood, O., Kang, S. & Cho, K · 2019
Cited alongside, same era.
PEPCONF, a diverse data set of peptide conformational energies
Robust and efficient implicit solvation model for fast semiempirical methods
Ehlert, S., Stahn, M., Spicher, S. & Grimme, S · 2021
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Understanding conformational entropy in small molecules
Chan, L., Morris, G. M. & Hutchison, G. R · 2021
Later among the works it cites.
Accurate de novo design of membrane-traversing macrocycles
Bhardwaj, G. et al · 2022
Later among the works it cites.
Incorporating NOE-Derived distances in conformer generation of cyclic peptides with distance geometry
Wang, S. et al · 2022
Later among the works it cites.
Spherical message passing for 3d molecular graphs
Liu, Y. et al · 2022
Later among the works it cites.
Geodiff: A geometric diffusion model for molecular conformation generation
Xu, M. et al · 2022
Later among the works it cites.
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Prasad, V. K., Otero-de-la Roza, A. & DiLabio, G. A · 2019
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GFN2-xTB-an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions
Bannwarth, C., Ehlert, S. & Grimme, S · 2019
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Methodologies for backbone macrocyclic peptide synthesis compatible with screening technologies
Shinbara, K., Liu, W., van Neer, R. H. P., Katoh, T. & Suga, H · 2020
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Directional message passing for molecular graphs
Gasteiger, J., Groß, J. & Günnemann, S · 2020
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A generative model for molecular distance geometry
Simm, G. & Hernandez-Lobato, J. M · 2020
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Torsional diffusion for molecular conformer generation
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High-resolution de novo structure prediction from primary sequence (2022)
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Anand, N. & Achim, T · 2022
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GEOM, energy-annotated molecular conformations for property prediction and molecular generation
Axelrod, S. & Gómez-Bombarelli, R · 2022
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Lessons for oral bioavailability: How conformationally flexible cyclic peptides enter and cross lipid membranes
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