Artificial intelligence guided conformational mining of intrinsically disordered proteins

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

Molecular dynamics with coupling to an external bath

Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A., & Haak, J. R. (1984) · The Journal of Chemical Physics · doi:10.1063/1.448118 · Cited by 30,626 · see citing works

Canonical sampling through velocity rescaling

Bussi, G., Donadio, D., & Parrinello, M. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2408420 · Cited by 18,034 · see citing works

TensorFlow: A system for large-scale machine learningPreprint

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works

ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB

Maier, J., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K., & Simmerling, C. L. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00255 · Cited by 10,971 · see citing works

CHARMM36m: An Improved Force Field for Folded and Intrinsically Disordered Proteins

Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmüller, H., & MacKerell, A. (2016) · Nature Methods · doi:10.1038/nmeth.4067 · Cited by 6,708 · see citing works

Improved side-chain torsion potentials for the Amber ff99SB protein force field

Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J., Dror, R., & Shaw, D. (2010) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22711 · Cited by 6,037 · see citing works

Accurate prediction of protein structures and interactions using a 3-track network

Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works

A general purpose model for the condensed phases of water: TIP4P/2005

Abascal, J. L. F., & Vega, C. (2005) · The Journal of Chemical Physics · doi:10.1063/1.2121687 · Cited by 3,934 · see citing works

Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald

Salomón–Ferrer, R., Götz, A. W., Poole, D., LeGrand, S., & Walker, R. C. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400314y · Cited by 3,373 · see citing works

Scalable molecular dynamics on CPU and GPU architectures with NAMD

Phillips, J. C., Hardy, D. J., Maia, J. D. C., Stone, J. E., Ribeiro, J. V., Bernardi, R. C., Buch, R., Fiorin, G., Hénin, J., Jiang, W., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0014475 · Cited by 3,123 · see citing works

The FoldX web server: an online force field

Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki387 · Cited by 2,759 · see citing works

An analysis of the accuracy of Langevin and molecular dynamics algorithms

Pastor, R. W., Brooks, B. R., & Szabó, A. (1988) · Molecular Physics · doi:10.1080/00268978800101881 · Cited by 1,046 · see citing works

Water Dispersion Interactions Strongly Influence Simulated Structural Properties of Disordered Protein States

Piana, S., Donchev, A., Robustelli, P., & Shaw, D. E. (2015) · The Journal of Physical Chemistry B · doi:10.1021/jp508971m · Cited by 855 · see citing works

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

Noé, F., Olsson, S., Köhler, J., & Wu, H. (2019) · Science · doi:10.1126/science.aaw1147 · Cited by 747 · see citing works

Replica exchange with solute tempering: a method for sampling biological systems in explicit water.

Liu, P., Kim, B., Friesner, R., & Berne, B. (2005) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0506346102 · Cited by 698 · see citing works

SHIFTX2: significantly improved protein chemical shift prediction

Han, B., Liu, Y., Ginzinger, S., & Wishart, D. S. (2011) · Journal of Biomolecular NMR · doi:10.1007/s10858-011-9478-4 · Cited by 673 · see citing works

VAMPnets for deep learning of molecular kinetics

Mardt, A., Pasquali, L., Wu, H., & Noé, F. (2017) · Nature Communications · doi:10.1038/s41467-017-02388-1 · Cited by 631 · see citing works

Deep-neural-network solution of the electronic Schrödinger equation

Hermann, J., Schätzle, Z., & Noé, F. (2020) · Nature Chemistry · doi:10.1038/s41557-020-0544-y · Cited by 559 · see citing works

Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life

Xue, B., Dunker, A. K., & Uversky, V. N. (2012) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2012.675145 · Cited by 557 · see citing works

How artificial intelligence is changing drug discovery

Fleming, N. (2018) · Nature · doi:10.1038/d41586-018-05267-x · Cited by 546 · see citing works

Amyloid-Beta: A Crucial Factor in Alzheimer's Disease

Sadigh‐Eteghad, S., Sabermarouf, B., Majdi, A., Talebi, M., Farhoudi, M., & Mahmoudi, J. (2014) · Medical Principles and Practice · doi:10.1159/000369101 · Cited by 542 · see citing works

FoXS, FoXSDock and MultiFoXS: Single-state and multi-state structural modeling of proteins and their complexes based on SAXS profiles

Schneidman‐Duhovny, D., Hammel, M., Tainer, J. A., & Šali, A. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw389 · Cited by 502 · see citing works

Polyglutamine Pathogenesis

Ross, C. A. (2002) · Neuron · doi:10.1016/s0896-6273(02)00872-3 · Cited by 500 · see citing works

‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures

Callaway, E. (2020) · Nature · doi:10.1038/d41586-020-03348-4 · Cited by 499 · see citing works

VAMPnets for deep learning of molecular kinetics

Mardt, A., Pasquali, L., Wu, H., & Noé, F. (2018) · RePEc: Research Papers in Economics · doi:10.17169/refubium-964 · Cited by 464 · see citing works

Balanced Protein–Water Interactions Improve Properties of Disordered Proteins and Non-Specific Protein Association

Best, R., Zheng, W.-W., & Mittal, J. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500569b · Cited by 312 · see citing works

Exploring Free-Energy Landscapes of Intrinsically Disordered Proteins at Atomic Resolution Using NMR Spectroscopy

Jensen, M. R., Zweckstetter, M., Huang, J., & Blackledge, M. J. (2014) · Chemical Reviews · doi:10.1021/cr400688u · Cited by 298 · see citing works

Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks

Saito, Y., Takamichi, S., & Saruwatari, H. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2761547 · Cited by 217 · see citing works

Molecular Details of Protein Condensates Probed by Microsecond Long Atomistic Simulations

Zheng, W., Dignon, G. L., Jovic, N., Xu, X., Regy, R. M., Fawzi, N. L., Kim, Y. C., Best, R. B., & Mittal, J. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c10489 · Cited by 213 · see citing works

Further along the Road Less Traveled: AMBER ff15ipq, an Original Protein Force Field Built on a Self-Consistent Physical Model

Debiec, K. T., Cerutti, D. S., Baker, L. R., Gronenborn, A. M., Case, D. A., & Chong, L. T. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00567 · Cited by 192 · see citing works

Introduction to Intrinsically Disordered Proteins (IDPs)

Uversky, V. N. (2014) · Chemical Reviews · doi:10.1021/cr500288y · Cited by 184 · see citing works

Variational encoding of complex dynamics

Hernández, C. X., Wayment-Steele, H. K., Sultan, M. M., Husic, B. E., & Pande, V. S. (2018) · Physical review. E · doi:10.1103/physreve.97.062412 · Cited by 176 · see citing works

POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins

Nielsen, J. T., & Mulder, F. A. A. (2018) · Journal of Biomolecular NMR · doi:10.1007/s10858-018-0166-5 · Cited by 164 · see citing works

Why Do Disordered and Structured Proteins Behave Differently in Phase Separation?

Zhou, H., Nguemaha, V., Mazarakos, K. P., & Qin, S. (2018) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2018.03.007 · Cited by 141 · see citing works

On easy implementation of a variant of the replica exchange with solute tempering in GROMACS

Terakawa, T., Kameda, T., & Takada, S. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21703 · Cited by 137 · see citing works

Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space

Degiacomi, M. T. (2018) · Structure · doi:10.1016/j.str.2019.03.018 · Cited by 126 · see citing works

Rapid boundary element solvation electrostatics calculations in folding simulations: Successful folding of a 23-residue peptide

Totrov, M. M., & Abagyan, R. A. (2001) · Biopolymers · doi:10.1002/1097-0282(2001)60:2 · Cited by 124 · see citing works

Deep clustering of protein folding simulations

Bhowmik, D., Gao, S. B., Young, M. T., & Ramanathan, A. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2507-5 · Cited by 88 · see citing works

Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins

Ramanathan, A., Ma, H., Parvatikar, A., & Chennubhotla, C. S. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.12.001 · Cited by 58 · see citing works

Application of SAXS for the Structural Characterization of IDPs

Kachala, M., Valentini, E., & Svergun, D. I. (2015) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-20164-1_8 · Cited by 53 · see citing works

Predicting new protein conformations from molecular dynamics simulation conformational landscapes and machine learning

Jin, Y., Johannissen, L. O., & Hay, S. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26068 · Cited by 41 · see citing works

Sequence-Dependent Backbone Dynamics of Intrinsically Disordered Proteins

Dey, S., MacAinsh, M., & Zhou, H. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00328 · Cited by 38 · see citing works

Using Dimensionality Reduction to Systematically Expand Conformational Sampling of Intrinsically Disordered Peptides

Kukharenko, O., Sawade, K., Steuer, J., & Peter, C. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00503 · Cited by 32 · see citing works

Sequence-Dependent Correlated Segments in the Intrinsically Disordered Region of ChiZ

Hicks, A., Escobar, C. A., Cross, T. A., & Zhou, H. (2020) · Biomolecules · doi:10.3390/biom10060946 · Cited by 29 · see citing works

Fuzzy Association of an Intrinsically Disordered Protein with Acidic Membranes

Hicks, A., Escobar, C. A., Cross, T. A., & Zhou, H. (2020) · JACS Au · doi:10.1021/jacsau.0c00039 · Cited by 26 · see citing works

Temperature-induced collapse of a disordered peptide observed by three sampling methods in molecular dynamics simulations

Hicks, A., & Zhou, H. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5027409 · Cited by 21 · see citing works

Multiscale Enhanced Sampling Using Machine Learning

Moritsugu, K. (2021) · Life · doi:10.3390/life11101076 · Cited by 11 · see citing works

AlphaFold, Artificial Intelligence (AI), and Allostery

Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works

Computer-Aided Drug Discovery for Undruggable Targets

Sun, Q., Wang, H., Xie, J., Wang, L., Mu, J., Li, J., Ren, Y., & Lai, L. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00969 · Cited by 69 · see citing works

Transferable deep generative modeling of intrinsically disordered protein conformations

Janson, G., & Feig, M. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012144 · Cited by 62 · see citing works

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Wang, L., Wen, Z., Liu, S.-W., Zhang, L., Finley, C., Lee, H., & Fan, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108620 · Cited by 53 · see citing works

Targeting protein disorder: the next hurdle in drug discovery

Lázár, T., Connor, A., DeLisle, C. F., Burger, V., & Tompa, P. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01220-6 · Cited by 43 · see citing works

Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data

Zhang, O., Haghighatlari, M., Li, J., Liu, Z. H., Namini, A., Teixeira, J. M. C., Forman-Kay, J. D., & Head-Gordon, T. L. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0141474 · Cited by 40 · see citing works

Rational drug design targeting intrinsically disordered proteins

Wang, H., Xiong, R., & Lai, L. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1685 · Cited by 39 · see citing works

Biomolecular Condensates as Emerging Biomaterials: Functional Mechanisms and Advances in Computational and Experimental Approaches

Zhu, Q., Raza, Z., Do‐Ha, D., Costa, E. D., Sasheva, P., McAlary, L., Mahmodi, H., Bowen, W. P., Ooi, L., Kabakova, I. V., et al. (2025) · Advanced Materials · doi:10.1002/adma.202510115 · Cited by 32 · see citing works

Machine Learning Generation of Dynamic Protein Conformational Ensembles

Zheng, L.-E., Barethiya, S., Nordquist, E. B., & Chen, J. (2023) · Molecules · doi:10.3390/molecules28104047 · Cited by 32 · see citing works

Atomistic molecular dynamics simulations of intrinsically disordered proteins

Muhammedkutty, F. N. K., MacAinsh, M., & Zhou, H. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103029 · Cited by 31 · see citing works

Beyond sequence: Structure-based machine learning

Durairaj, J., de Ridder, D., & van Dijk, A. D. J. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.039 · Cited by 31 · see citing works

Structural ensembles of disordered proteins from hierarchical chain growth and simulation

Pietrek, L. M., Stelzl, L. S., & Hummer, G. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102501 · Cited by 29 · see citing works

Machine learning methods to study sequence–ensemble–function relationships in disordered proteins

von Bülow, S., Tesei, G., & Lindorff‐Larsen, K. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103028 · Cited by 27 · see citing works

Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies

Ellaway, J. I. J., Anyango, S., Nair, S. S., Zaki, H. A., Nadzirin, N., Powell, H. R., Gutmanas, A., Váradi, M., & Velankar, S. S. (2024) · Structural Dynamics · doi:10.1063/4.0000251 · Cited by 27 · see citing works

Exploring the Conformational Ensembles of Protein–Protein Complex with Transformer-Based Generative Model

(2024) · doi:10.1021/acs.jctc.4c00255 · Cited by 26 · see citing works

Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder

Zhu, J.-J., Zhang, N.-J., Wei, T., & Chen, H. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24086896 · Cited by 26 · see citing works

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

Ruzmetov, T., Hung, T. I., Jonnalagedda, S. P., Chen, S., Fasihianifard, P., Guo, Z., Bhanu, B., & Chang, C. A. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01838 · Cited by 21 · see citing works

Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling

Zhu, J., Li, Z., Tong, H., Lu, Z., Zhang, N.-J., Wei, T., & Chen, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad429 · Cited by 22 · see citing works

Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs

Sil, S., Datta, I., & Basu, S. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1542267 · Cited by 20 · see citing works

Molecular Simulations Integrated with Experiments for Probing the Interaction Dynamics and Binding Mechanisms of Intrinsically Disordered Proteins

Ghosh, C., Nagpal, S., & Muñoz, V. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102756 · Cited by 16 · see citing works

Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning

Aupič, J., Pokorná, P., Ruthstein, S., & Magistrato, A. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c01544 · Cited by 18 · see citing works

Generation of protein dynamics by machine learning

Janson, G., & Feig, M. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103115 · Cited by 18 · see citing works

Machine-learning-based methods to generate conformational ensembles of disordered proteins

Taneja, I., & Lasker, K. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2023.12.001 · Cited by 18 · see citing works

Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution

Bruyn, E. D., Dorn, A. E., Rossetti, G., Fernández, C. O., Outeiro, T. F., Schulz, J. B., & Carloni, P. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01172 · Cited by 13 · see citing works

Critical Assessment of Self-Consistency Checks in the All-Atom Molecular Dynamics Simulation of Intrinsically Disordered Proteins

Gaalswyk, K., Haider, A., & Ghosh, K. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01140 · Cited by 12 · see citing works

Decoding intrinsically disordered regions in biomolecular condensates

Shi, M., Wu, Z., Zhang, Y., & Li, T. (2025) · Fundamental Research · doi:10.1016/j.fmre.2025.01.013 · Cited by 12 · see citing works

Complex Conformational Space of the RNA Polymerase II C-Terminal Domain upon Phosphorylation

Amith, W. D., & Dutagaci, B. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c02655 · Cited by 10 · see citing works

Physics-Based Machine Learning Trains Hamiltonians and Decodes the Sequence–Conformation Relation in the Disordered Proteome

Houston, L., Phillips, M., Torres, A., Gaalswyk, K., & Ghosh, K. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01114 · Cited by 9 · see citing works

Myofilament-associated proteins with intrinsic disorder (MAPIDs) and their resolution by computational modeling

Sun, B., & Kekenes-Huskey, P. M. (2023) · Quarterly Reviews of Biophysics · doi:10.1017/s003358352300001x · Cited by 9 · see citing works

Dissecting the Molecular Determinants of α‐synuclein Phase Separation and Condensate Aging: The Pivotal Role of β‐Sheet‐Rich Motifs

Tang, Y., Wen, J., Yang, Z., Yao, Y., He, S., Zeng, J., Dong, X., Qiao, Q., Zhou, Y., Perrett, S., et al. (2025) · Advanced Science · doi:10.1002/advs.202511545 · Cited by 7 · see citing works

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

唐, Y. 一. T., 杨, Z. 中. Y., 姚, Y. 逸. Y., 周, Y. 运. Z., 谈, Y. 圆. T., 王, Z. 子. W., 潘, T. 瞳. P., 熊, R. 瑞. X., 孙, J. 俊. S., & Wei, G. (2024) · Chinese Physics B · doi:10.1088/1674-1056/ad1a92 · Cited by 7 · see citing works

Initial Investigations of Intrinsically Disordered Regions in Inherited Retinal Diseases

Lee, K. E., Procopio, R. A., Pulido, J. S., & Gunton, K. B. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24021060 · Cited by 6 · see citing works

How good is generative diffusion model for enhanced sampling of protein conformations across scales and in all-atom resolution?

Bera, P., & Mondal, J. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0279756 · Cited by 7 · see citing works

Advances in the determination of disordered protein ensemble

Ghafouri, H., Tosatto, S. C. E., & Monzón, A. M. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103198 · Cited by 5 · see citing works

Characterizing Structural and Kinetic Ensembles of Intrinsically Disordered Proteins Using Writhe

(2025) · doi:10.1021/acs.jctc.5c01133 · Cited by 5 · see citing works

Solution of the protein structure prediction problem at last: crucial innovations and next frontiers

Agard, D. A., Bowman, G. R., DeGrado, W. F., Dokholyan, N. V., & Zhou, H. (2022) · Faculty Reviews · doi:10.12703/r-01-0000020 · Cited by 5 · see citing works

Deep Generative Modeling of Protein Conformations: A Comprehensive Review

Dao, T. M., & Rahman, T. (2025) · BioChem · doi:10.3390/biochem5030032 · Cited by 5 · see citing works

Application of computational methods in the drug discovery and development of Alzheimer's disease

Zhang, Q., Gong, X., Liu, H., & Yao, X. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.038 · Cited by 4 · see citing works

Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins

Coskuner‐Weber, O., Gentili, P. L., & Uversky, V. N. (2025) · Biophysical Reviews · doi:10.1007/s12551-025-01286-x · Cited by 4 · see citing works

Advancements in characterization of protein dynamics with machine learning

Madani, M., & Tarakanova, A. (2026) · npj Soft Matter · doi:10.1038/s44431-026-00025-w · Cited by 3 · see citing works

SPEADI: Accelerated Analysis of IDP-Ion Interactions from MD-Trajectories

Bruyn, E. D., Dorn, A. E., Zimmermann, O., & Rossetti, G. (2023) · Biology · doi:10.3390/biology12040581 · Cited by 3 · see citing works

Encoding the space of protein-protein binding interfaces by artificial intelligence

Su, Z., Dhusia, K., & Wu, Y. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108080 · Cited by 3 · see citing works

Artificial intelligence-based protein structure prediction and systems biology-guided smart drug screening

Chandra, A., Goyal, N., Behera, A., Samanta, J., & Sharma, T. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-16013-4.00016-6 · Cited by 3 · see citing works

Combining All-Atom Molecular Dynamics Simulation and NMR to Analyze Conformational Ensemble of Intrinsically Disordered Proteins

Song, X., & Wang, W. (2025) · Communications in Computational Chemistry · doi:10.4208/cicc.2025.217.02 · Cited by 2 · see citing works

Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous SolutionPreprint

Bruyn, E. D., Dorn, A. E., Rossetti, G., Fernández, C. O., Outeiro, T. F., Schulz, J. B., & Carloni, P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.10.531864 · Cited by 2 · see citing works

Efficient and Accelerated Protein Free Energy Landscape Reconstruction via Conditional Variational Autoencoders

Shen, R., Zhao, M., Wang, W., & Dong, H. (2026) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.6c00485 · Cited by 1 · see citing works

Generative Machine Learning of Conformational Ensembles of Intrinsically Disordered Proteins: Progress and Opportunities

Roy, I., & Mondal, J. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00354 · Cited by 1 · see citing works

In search of shape in the unshaped: Constructing ensembles of intrinsically disordered proteins

Saha, D., Yang, W., & Zheng, W. (2026) · Biophysics Reviews · doi:10.1063/5.0307183 · Cited by 1 · see citing works

Studying the Signaling Mechanism of Neuropilin-1’s Intracellular Disorder Region via Conformational Mining and Dynamic Interaction Characterization

Yue, C., Shi, S., Li, Z., & Ye, S. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c07616 · Cited by 1 · see citing works

Employing Artificial Neural Networks for Optimal Storage and Facile Sharing of Molecular Dynamics Simulation Trajectories

Wasim, A., Schäfer, L. V., & Mondal, J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01294 · Cited by 1 · see citing works

Emerging Frontiers in Conformational Exploration of Disordered Proteins: Integrating Autoencoder and Molecular Simulations

Zeng, J., Yang, Z., Tang, Y., & Wei, G. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.4c00670 · Cited by 1 · see citing works

An integrative approach to understand structural dynamics of protein molecular machines across spatiotemporal scales

Zhu, Y., Gong, Z., & Tang, C. (2024) · Scientia Sinica Chimica · doi:10.1360/ssc-2023-0247

Neurofilament Light Disordered Tail Mutations Reshape Its Self-Assembled Network Structure

Aodeh, R., Dan, Y., Yona, D., Shalabi, M., Sivan, A., Kravicas, M., Aharoni, H., Koren, G., Adler‐Abramovich, L., & Beck, R. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.6c03785

DreamFold : A World Model to efficiently generate protein folding pathways in the latent space

Ianeselli, A., Im, J., Cavallin, E., & Gerstein, M. (2026) · Protein Science · doi:10.1002/pro.70794

Challenges and opportunities of AI-based approaches for intrinsically disordered proteins

Ozbas, C. E., Akkum, F. I., Damar, M., Uversky, V. N., & Coskuner‐Weber, O. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44536-1.00004-3

Revealing protein structural transitions in the era of generative artificial intelligence

Hung, T. I., Venkatesan, R., & Chang, C. A. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2026.103421

Generative Machine Learning of Conformational Ensembles of Intrinsically Disordered Proteins: Progress and Opportunities

Roy, I., & Mondal, J. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00354 · Cited by 1 · see citing works

Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs

Sil, S., Datta, I., & Basu, S. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1542267 · Cited by 20 · see citing works

Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder

Zhu, J.-J., Zhang, N.-J., Wei, T., & Chen, H. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24086896 · Cited by 26 · see citing works

Emerging Frontiers in Conformational Exploration of Disordered Proteins: Integrating Autoencoder and Molecular Simulations

Zeng, J., Yang, Z., Tang, Y., & Wei, G. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.4c00670 · Cited by 1 · see citing works

Direct generation of protein conformational ensembles via machine learning

Janson, G., Valdes-Garcia, G., Heo, L., & Feig, M. (2023) · Nature Communications · doi:10.1038/s41467-023-36443-x · Cited by 192 · see citing works