Molecular dynamics simulations of biomolecules

Climatic Impact of Tropical Lowland Deforestation on Nearby Montane Cloud Forests

Lawton, R. O., Nair, U. S., Pielke, R. A., & Welch, R. M. (2001) · Science · doi:10.1126/science.1062459 · Cited by 1,169

Machine Learning Force Fields

Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647

X-ray solution scattering (SAXS) combined with crystallography and computation: defining accurate macromolecular structures, conformations and assemblies in solution

Putnam, C. D., Hammel, M., Hura, G. L., & Tainer, J. A. (2007) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583507004635 · Cited by 1,184

Unified rational protein engineering with sequence-based deep representation learning

Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131

Enhanced sampling techniques in molecular dynamics simulations of biological systems

Bernardi, R. C., Melo, M. C. R., & Schulten, K. (2015) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2014.10.019 · Cited by 757

Lipids on the move: Simulations of membrane pores, domains, stalks and curves

Marrink, S. J., de Vries, A. H., & Tieleman, D. P. (2009) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2008.10.006 · Cited by 440

To milliseconds and beyond: challenges in the simulation of protein folding

Lane, T. J., Shukla, D., Beauchamp, K. A., & Pande, V. S. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.11.002 · Cited by 409

Overcoming mutation-based resistance to antiandrogens with rational drug design

Balbas, M. D., Evans, M. J., Hosfield, D. J., Wongvipat, J., Arora, V. K., Watson, P. A., Chen, Y., Greene, G. L., Shen, Y., & Sawyers, C. L. (2013) · eLife · doi:10.7554/elife.00499 · Cited by 397

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

Chen, S., Lin, T., Basu, R., Ritchey, J., Wang, S., Luo, Y., Li, X., Pei, D., Kara, L. B., & Cheng, X. (2024) · Nature Communications · doi:10.1038/s41467-024-45766-2 · Cited by 324

AI in drug discovery and its clinical relevance

Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319

Progress in Modeling of Protein Structures and Interactions.

Schueler-Furman, O., Wang, C., Bradley, P., Misura, K., & Baker, D. (2005) · Science · doi:10.1126/science.1112160 · Cited by 294

Thermal processing of diblock copolymer melts mimics metallurgy

Kim, K., Schulze, M. W., Arora, A., Lewis, R. M., Hillmyer, M. A., Dorfman, K. D., & Bates, F. S. (2017) · Science · doi:10.1126/science.aam7212 · Cited by 295

Deciphering conformational transitions of proteins by small angle X-ray scattering and normal mode analysis

Panjkovich, A., & Svergun, D. I. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c5cp04540a · Cited by 204

Computational prediction of protein–protein binding affinities

Siebenmorgen, T., & Zacharias, M. (2020) · WIREs Computational Molecular Science · doi:10.1002/wcms.1448 · Cited by 184

Biomolecular simulations: from dynamics and mechanisms to computational assays of biological activity

Huggins, D. J., Biggin, P. C., Dämgen, M. A., Essex, J. W., Harris, S. A., Henchman, R. H., Khalid, S., Kuzmanic, A., Laughton, C. A., Michel, J., et al. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1393 · Cited by 169

Relation between native ensembles and experimental structures of proteins

Best, R. B., Lindorff-Larsen, K., DePristo, M. A., & Vendruscolo, M. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0511156103 · Cited by 152

Exploring the role of receptor flexibility in structure-based drug discovery

Feixas, F., Lindert, S., Sinko, W., & McCammon, J. A. (2014) · Biophysical Chemistry · doi:10.1016/j.bpc.2013.10.007 · Cited by 156

Characterization of the free-energy landscapes of proteins by NMR-guided metadynamics

Granata, D., Camilloni, C., Vendruscolo, M., & Laio, A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218350110 · Cited by 143

Drug design on quantum computers

(2024) · doi:10.1038/s41567-024-02411-5 · Cited by 132

Catalytic Contributions from Remote Regions of Enzyme Structure

Lee, J., & Goodey, N. M. (2011) · Chemical Reviews · doi:10.1021/cr100042n · Cited by 116

Potential neutralizing antibodies discovered for novel corona virus using machine learning

Magar, R., Yadav, P., & Barati Farimani, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-84637-4 · Cited by 114

Kinetic characterization of the critical step in HIV-1 protease maturation

Sadiq, S. K., Noé, F., & De Fabritiis, G. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1210983109 · Cited by 102

Membranes serve as allosteric activators of phospholipase A 2 , enabling it to extract, bind, and hydrolyze phospholipid substrates

Mouchlis, V. D., Bucher, D., McCammon, J. A., & Dennis, E. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1424651112 · Cited by 97

G-protein coupled receptors: advances in simulation and drug discovery

Miao, Y., & McCammon, J. A. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.06.008 · Cited by 93

Integrative structure modeling of macromolecular assemblies from proteomics data

Lasker, K., Phillips, J. L., Russel, D., Velázquez-Muriel, J., Schneidman-Duhovny, D., Tjioe, E., Webb, B., Schlessinger, A., & Sali, A. (2010) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r110.000067 · Cited by 71

Protein structure prediction by AlphaFold 2: are attention and symmetries all you need?

Bouatta, N., Sorger, P., & AlQuraishi, M. (2021) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798321007531 · Cited by 69

Methods in Statistical Kinetics

Moffitt, J. R., Chemla, Y. R., & Bustamante, C. (2010) · Methods in Enzymology · doi:10.1016/s0076-6879(10)75010-2 · Cited by 66

Application of Quantum Computing to Biochemical Systems: A Look to the Future

Cheng, H.-P., Deumens, E., Freericks, J. K., Li, C., & Sanders, B. A. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.587143 · Cited by 53

Energy Landscape of the Prion Protein Helix 1 Probed by Metadynamics and NMR

Camilloni, C., Schaal, D., Schweimer, K., Schwarzinger, S., & De Simone, A. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2011.12.003 · Cited by 45

Energy penalties enhance flexible receptor docking in a model cavity

Kamenik, A. S., Singh, I., Lak, P., Balius, T. E., Liedl, K. R., & Shoichet, B. K. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106195118 · Cited by 32

Molecular Mechanism of Brassinosteroid Perception by the Plant Growth Receptor BRI1

Aldukhi, F., Deb, A., Zhao, C., Moffett, A. S., & Shukla, D. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.9b09377 · Cited by 29

Characterization of Protein Flexibility Using Small-Angle X-Ray Scattering and Amplified Collective Motion Simulations

Wen, B., Peng, J., Zuo, X., Gong, Q., & Zhang, Z. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2014.07.005 · Cited by 20

DeepPath: overcoming data scarcity for protein transition pathway prediction using physics-based deep learning

Pang, Y. T., Yang, L., Kuo, K. M., & Gumbart, J. C. (2026) · Chemical Science · doi:10.1039/d5sc08253f · Cited by 13

Computational prediction of high-risk non-synonymous SNPs in human ApoE and their structural impact on amyloid-β interaction in Alzheimer’s disease pathogenesis

Hossain, M. M., Adhikari, J., Dutta, A., Khandaker, A., Islam, S., Rahman, M. M., & Shibly, A. Z. (2025) · PLOS One · doi:10.1371/journal.pone.0331339 · Cited by 11

Ensemble-based characterization of unbound and bound states on protein energy landscape

Ruvinsky, A. M., Kirys, T., Tuzikov, A. V., & Vakser, I. A. (2013) · Protein Science · doi:10.1002/pro.2256 · Cited by 9

Integrative spatiotemporal modeling of biomolecular processes: Application to the assembly of the nuclear pore complex

Latham, A. P., Zhang, W., Tempkin, J. O. B., Otsuka, S., Ellenberg, J., & Sali, A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415674122 · Cited by 8

Optimizing reaction coordinate by flux maximization in the transition path ensemble

Li, W. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0079390 · Cited by 5

Transforming a fragile protein helix into an ultrastable scaffold via a hierarchical AI and chemistry framework

Qiu, J., Tang, G., Feng, T., Zheng, B., Liu, Y., & Zheng, P. (2026) · eLife · doi:10.7554/elife.109753.3 · Cited by 3

Using Deep Graph Neural Networks Improves Physics-Based Hydration Free Energy Predictions Even for Molecules Outside of the Training Set Distribution

Elder, L. H., & Onufriev, A. V. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c02263 · Cited by 2

Time-Lagged Flux in the Transition Path Ensemble: Flux Maximization and Relation to Transition Path Theory

(2022) · doi:10.1021/acs.jpca.2c02221 · Cited by 2

Transforming a Fragile Protein Helix into an Ultrastable Scaffold via a Hierarchical AI and Chemistry Framework

Qiu, J., Tang, G., Feng, T., Zheng, B., Liu, Y., & Zheng, P. (2025) · doi:10.1101/2025.11.03.686355 · Cited by 1

Explainable Machine Learning Guided Enhanced Sampling of Protein Conformational Transition in HSP90

Chatterjee, S., & Ray, D. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00132

Potential Energy Weighted Reactive Flux and Total Rate of Change of Potential Energy: Theory and Illustrative Applications

(2022) · doi:10.1021/acs.jpca.2c04886