A highly accurate metadynamics-based Dissociation Free Energy method to calculate protein–protein and protein–ligand binding potencies

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

Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments

Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9644-8 · Cited by 6,130 · see citing works

Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters

Bowers, K., Chow, E., Xu, H.-G., Dror, R., Eastwood, M., Gregersen, B. A., Klepeis, J., Kolossváry, I., Moraes, M. A., Sacerdoti, F. D., et al. (2006) · International Conference on Software Composition · doi:10.1145/1188455.1188544 · Cited by 3,905 · see citing works

Highly accurate protein structure prediction for the human proteome

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · see citing works

OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins.

Harder, E. D., Damm, W., Maple, J., Wu, C., Reboul, M., Xiang, J.-Y., Wang, L.-L., Lupyan, D., Dahlgren, M. K., Knight, J. L., et al. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00864 · Cited by 2,826 · see citing works

End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

Wang, E., Sun, H., Wang, J., Wang, Z., Liu, H., Zhang, J. Z. H., & Hou, T. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00055 · Cited by 2,266 · see citing works

Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field

Wang, L., Wu, Y., Deng, Y., Kim, B., Pierce, L., Krilov, G., Lupyan, D., Robinson, S., Dahlgren, M. K., Greenwood, J., et al. (2015) · Journal of the American Chemical Society · doi:10.1021/ja512751q · Cited by 1,313 · see citing works

The statistical-thermodynamic basis for computation of binding affinities: a critical review

Gilson, M. K., Given, J. A., Bush, B. L., & McCammon, J. A. (1997) · Biophysical Journal · doi:10.1016/s0006-3495(97)78756-3 · Cited by 1,222 · see citing works

Metadynamics

Barducci, A., Bonomi, M., & Parrinello, M. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.31 · Cited by 1,217 · see citing works

Ultra-large library docking for discovering new chemotypes

Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., et al. (2019) · Nature · doi:10.1038/s41586-019-0917-9 · Cited by 998 · see citing works

Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution

Greenwood, J. R., Calkins, D., Sullivan, A. P., & Shelley, J. C. (2010) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-010-9349-1 · Cited by 967 · see citing works

Computer-based de novo design of drug-like molecules

Schneider, G., & Fechner, U. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1799 · Cited by 956 · see citing works

Lessons in PROTAC Design from Selective Degradation with a Promiscuous Warhead

Bondeson, D. P., Smith, B. E., Burslem, G. M., Buhimschi, A. D., Hines, J., Jaime-Figueroa, S., Wang, J., Hamman, B. D., Ishchenko, A., & Crews, C. M. (2018) · Cell Chemical Biology · doi:10.1016/j.chembiol.2017.09.010 · Cited by 923 · see citing works

PROTAC-induced BET protein degradation as a therapy for castration-resistant prostate cancer

Raina, K., Lu, J., Qian, Y., Altieri, M., Gordon, D., Rossi, A. M. K., Wang, J., Chen, X., Dong, H., Siu, K., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1521738113 · Cited by 852 · see citing works

The Advantages of Targeted Protein Degradation Over Inhibition: An RTK Case Study

Burslem, G. M., Smith, B. E., Lai, A. C., Jaime-Figueroa, S., McQuaid, D. C., Bondeson, D. P., Toure, M., Dong, H., Qian, Y., Wang, J., et al. (2018) · Cell Chemical Biology · doi:10.1016/j.chembiol.2017.09.009 · Cited by 686 · see citing works

Using metadynamics to explore complex free-energy landscapes

Bussi, G., & Laio, A. (2020) · Nature Reviews Physics · doi:10.1038/s42254-020-0153-0 · Cited by 613 · see citing works

Differential PROTAC substrate specificity dictated by orientation of recruited E3 ligase

Smith, B. E., Wang, S. L., Jaime-Figueroa, S., Harbin, A., Wang, J., Hamman, B. D., & Crews, C. M. (2019) · Nature Communications · doi:10.1038/s41467-018-08027-7 · Cited by 517 · see citing works

Artificial intelligence in drug discovery: recent advances and future perspectives

Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1909567 · Cited by 464 · see citing works

Efficient Exploration of Chemical Space with Docking and Deep Learning

Ying, Y., Yao, K., Repasky, M. P., Leswing, K., Abel, R., Shoichet, B. K., & Jerome, S. V. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00810 · Cited by 454 · see citing works

Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2

Vreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jiménez-García, B., Bates, P. A., Fernandez-Recio, J., et al. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.016 · Cited by 424 · see citing works

Funnel metadynamics as accurate binding free-energy method

Limongelli, V., Bonomi, M., & Parrinello, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1303186110 · Cited by 409 · see citing works

Androgen receptor degradation by the proteolysis-targeting chimera ARCC-4 outperforms enzalutamide in cellular models of prostate cancer drug resistance

Salami, J., Alabi, S., Willard, R. R., Vitale, N. J., Wang, J., Dong, H., Jin, M., McDonnell, D. P., Crew, A. P., Neklesa, T. K., et al. (2018) · Communications Biology · doi:10.1038/s42003-018-0105-8 · Cited by 349 · see citing works

A structure‐based benchmark for protein–protein binding affinity

Kastritis, P., Moal, I., Hwang, H., Weng, Z., Bates, P. A., Bonvin, A., & Janin, J. (2011) · Protein Science · doi:10.1002/pro.580 · Cited by 318 · see citing works

Flexible Docking in Solution Using Metadynamics

Gervasio, F. L., Laio, A., & Parrinello, M. (2005) · Journal of the American Chemical Society · doi:10.1021/ja0445950 · Cited by 287 · see citing works

Are Scoring Functions in Protein−Protein Docking Ready To Predict Interactomes? Clues from a Novel Binding Affinity Benchmark

Kastritis, P. L., & Bonvin, A. M. J. J. (2010) · Journal of Proteome Research · doi:10.1021/pr9009854 · Cited by 246 · see citing works

Efficient Determination of Protein–Protein Standard Binding Free Energies from First Principles

Gumbart, J. C., Roux, B., & Chipot, C. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400273t · Cited by 211 · see citing works

Computational prediction of protein–protein binding affinities

Siebenmorgen, T., & Zacharias, M. (2020) · WIREs Computational Molecular Science · doi:10.1002/wcms.1448 · Cited by 184 · cited 6× · see citing works

Ligand binding free-energy calculations with funnel metadynamics

Raniolo, S., & Limongelli, V. (2020) · Nature Protocols · doi:10.1038/s41596-020-0342-4 · Cited by 166 · see citing works

Proteins Feel More Than They See: Fine-Tuning of Binding Affinity by Properties of the Non-Interacting Surface

Kastritis, P. L., Rodrigues, J., Folkers, G. E., Boelens, R., & Bonvin, A. M. J. J. (2014) · Journal of Molecular Biology · doi:10.1016/j.jmb.2014.04.017 · Cited by 143 · see citing works

N501Y and K417N Mutations in the Spike Protein of SARS-CoV-2 Alter the Interactions with Both hACE2 and Human-Derived Antibody: A Free Energy of Perturbation Retrospective Study

Fratev, F. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01242 · Cited by 95 · see citing works

Scoring functions for protein–protein interactions

Moal, I. H., Moretti, R., Baker, D. A., & Fernández‐Recio, J. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.06.017 · Cited by 91 · see citing works

Relative binding affinity prediction of charge-changing sequence mutations with FEP in protein-protein interfaces

Clark, A. J., Negron, C., Hauser, K., Sun, M., Wang, L., Abel, R., & Friesner, R. A. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.02.003 · Cited by 91 · see citing works

Prediction of protein–protein binding free energies

Vreven, T., Hwang, H., Pierce, B. G., & Weng, Z. (2012) · Protein Science · doi:10.1002/pro.2027 · Cited by 76 · see citing works

Accelerating the Calculation of Protein–Ligand Binding Free Energy and Residence Times Using Dynamically Optimized Collective Variables

Brotzakis, Z. F., Limongelli, V., & Parrinello, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00934 · Cited by 74 · see citing works

An Improved Free Energy Perturbation FEP+ Sampling Protocol for Flexible Ligand-Binding Domains

Fratev, F., & Sirimulla, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-53133-1 · Cited by 73 · see citing works

De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

Santana, M. V., & Silva, F. P. (2021) · BMC Chemistry · doi:10.1186/s13065-021-00737-2 · Cited by 69 · see citing works

The N501Y and K417N mutations in the spike protein of SARS-CoV-2 alter the interactions with both hACE2 and human derived antibody: A Free energy of perturbation studyPreprint

Fratev, F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.23.424283 · Cited by 67 · see citing works

Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations

Duan, J., Lupyan, D., & Wang, L. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.05.020 · Cited by 63 · see citing works

Prediction of protein–protein complexes using replica exchange with repulsive scaling

Siebenmorgen, T., Engelhard, M., & Zacharias, M. (2020) · Journal of Computational Chemistry · doi:10.1002/jcc.26187 · Cited by 50 · see citing works

Automated De Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn?

Grebner, C., Matter, H., Plowright, A. T., & Heßler, G. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02044 · Cited by 47 · see citing works

Comparing alchemical and physical pathway methods for computing the absolute binding free energy of charged ligands

Deng, N., Cui, D., Zhang, B. W., Xia, J., Cruz, J., & Levy, R. M. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp01524d · Cited by 42 · see citing works

Free Energy Calculations for Protein–Ligand Binding Prediction

Jespers, W., Åqvist, J., & Gutiérrez‐de‐Terán, H. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1209-5_12 · Cited by 35 · see citing works

Comprehensive evaluation of the MM-GBSA method on bromodomain-inhibitor sets

Çınaroğlu, S. S., & Timuçin, E. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz143 · Cited by 31 · see citing works

Exploring the Free-Energy Landscape and Thermodynamics of Protein-Protein Association

Tse, C., Wickstrom, L., Kvaratskhelia, M., Gallicchio, E., Levy, R. M., & Deng, N. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.08.005 · Cited by 30 · see citing works

Protein-Ligand Interactions and Drug Design

(2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1209-5 · Cited by 28 · see citing works

Chasing molecular glue degraders: screening approaches

Domostegui, A., Nieto-Barrado, L., Perez-Lopez, C., & Mayor-Ruiz, C. (2022) · Chemical Society Reviews · doi:10.1039/d2cs00197g · Cited by 200 · see citing works

Computational Approaches to Predict Protein–Protein Interactions in Crowded Cellular Environments

Grassmann, G., Miotto, M., Desantis, F., Rienzo, L. D., Tartaglia, G. G., Pastore, A., Ruocco, G., Monti, M. M., & Milanetti, E. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00550 · Cited by 106 · see citing works

Protein–protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction

Rui, H., Ashton, K. S., Min, J., Wang, C. W., & Potts, P. R. (2023) · RSC Chemical Biology · doi:10.1039/d2cb00207h · Cited by 83 · see citing works

Enhanced sampling in molecular dynamics simulations and their latest applications—A review

Shen, W., Zhou, T., & Shi, X. (2023) · Nano Research · doi:10.1007/s12274-023-6311-9 · Cited by 44 · see citing works

Hazardous Shortcuts in Standard Binding Free Energy Calculations

Blazhynska, M., Goulard Coderc de Lacam, E., Chen, H., Roux, B., & Chipot, C. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c01490 · Cited by 27 · see citing works

Design of Novel Coumarin Derivatives as NUDT5 Antagonists That Act by Restricting ATP Synthesis in Breast Cancer Cells

Niranjan, V., Jayaprasad, S., Uttarkar, A., Kusanur, R. A., & Kumar, J. (2022) · Molecules · doi:10.3390/molecules28010089 · Cited by 27 · see citing works

Standard Binding Free-Energy Calculations: How Far Are We from Automation?

Fu, H., Chipot, C. J., Shao, X., & Cai, W. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c04370 · Cited by 18 · see citing works

Estimating Absolute Protein–Protein Binding Free Energies by a Super Learner Model

Chaves, E. J. F., Sartori, J., Santos, W. M., da Cruz, C. H. B., Mhrous, E. N., Nacimento-Filho, M. F., Ferraz, M. V. F., & Lins, R. D. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01641 · Cited by 17 · see citing works

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

Coste, A., Slejko, E., Zavadlav, J., & Praprotnik, M. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00984 · Cited by 15 · see citing works

Challenges and frontiers of computational modelling of biomolecular recognition

Wang, J., Bhattarai, A., Do, H. N., & Miao, Y. (2022) · QRB Discovery · doi:10.1017/qrd.2022.11 · Cited by 15 · see citing works

Interaction of Some Amino-Nitrile Derivatives with Vascular Endothelial Growth Factor Receptor 1 (VEGFR1) Using a Theoretical Model

Lauro, F., Díaz-Cedillo, F., Marcela, R., Alvarez-Ramirez, M., Mateu-Armad, M. V., Maria, L., & López-Gutierrez, T. (2023) · Drug Research · doi:10.1055/a-2062-3571 · Cited by 13 · see citing works

Assessing Metadynamics and Docking for Absolute Binding Free Energy Calculations Using Severe Acute Respiratory Syndrome Coronavirus 2 Main Protease Inhibitors

Saar, A., Ghahremanpour, M. M., Tirado‐Rives, J., & Jorgensen, W. L. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01453 · Cited by 12 · see citing works

De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein

Niranjan, V., Uttarkar, A., Ramakrishnan, A., Muralidharan, A., Shashidhara, A., Acharya, A., Tarani, A., & Kumar, J. (2023) · Current Issues in Molecular Biology · doi:10.3390/cimb45050271 · Cited by 11 · see citing works

Mycobacterium Time-Series Genome Analysis Identifies AAC2′ as a Potential Drug Target with Naloxone Showing Potential Bait Drug Synergism

Niranjan, V., Uttarkar, A., Murali, K., Niranjan, S., Jayalatha, G., & Kumar, J. (2022) · Molecules · doi:10.3390/molecules27196150 · Cited by 11 · see citing works

ModBind, a Rapid Simulation-Based Predictor of Ligand Binding and Off-Rates

Sinko, W., Mertz, B., Shimizu, T., Takahashi, T., Terada, Y., & Kimura, S. R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01805 · Cited by 10 · see citing works

From Apo to Ligand-Bound: Unraveling PPARγ-LBD Conformational Shifts via Advanced Molecular Dynamics

Falbo, E., Delre, P., & Lavecchia, A. (2025) · ACS Omega · doi:10.1021/acsomega.4c11128 · Cited by 11 · see citing works

Computational generation and characterization of IsdA‐binding aptamers with single‐molecule FRET analysis

Sabbih, G. O., Wijesinghe, K. M., Algama, C. H., Dhakal, S., & Danquah, M. K. (2023) · Biotechnology Journal · doi:10.1002/biot.202300076 · Cited by 10 · see citing works

Targeting fusion oncoproteins in childhood cancers: challenges and future opportunities for developing therapeutics

Verma, S. K., Witkin, K. L., Sharman, A., & Smith, M. A. (2024) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djae075 · Cited by 9 · see citing works

MnM-W-MMGBSA: A Computational Strategy to Improve Relative Binding Free Energies of Protein–Protein Interaction Systems

Hasan, M., Sharma, S., Mallen, J. J., & Saha, A. (2025) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c04085 · Cited by 9 · see citing works

Molecular Dynamics Simulations in Protein–Protein Docking

Cieślak, D., Kabelka, I., & Bartuzi, D. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3985-6_6 · Cited by 8 · see citing works

Theoretical investigation of hydroxylated analogues of valinomycin as potassium transporter

Sessa, L., Concilio, S., Marrafino, F., Sarkar, A., Diana, R., & Piotto, S. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107936 · Cited by 6 · see citing works

Quantum Mechanics-Based Ranking of Predicted Proteolysis Targeting Chimeras-Mediated Ternary Complexes

Monteleone, S., Morao, I., Fedorov, D. G., & Kellici, T. F. (2025) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.4c00534 · Cited by 5 · see citing works

Novel macromolecule CPD4 suppresses cell proliferation and metastasis of triple-negative breast cancer by targeting ROR1 protein

Ta, T. M., Reed, V. L., Gupta, S. R. R., Khan, M., Chandra, N., Dwyer, N., Fultang, N., Singh, I. K., & Peethambaran, B. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.143301 · Cited by 5 · see citing works

Perspective on Integrative Simulations of Bioenergetic Domains

Pirnia, M. M., Maqdisi, R., Mittal, S., Şener, M., & Singharoy, A. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c07335 · Cited by 5 · see citing works

From code to cure: computational identification of LasR inhibitors to combat quorum sensing in P. aeruginosa

Chowdhury, S., Kumar, M., Rawat, S., Singh, S., & Kaur, P. (2025) · Molecular Diversity · doi:10.1007/s11030-025-11333-0 · Cited by 4 · see citing works

Computational Insights into Papaveroline as an In Silico Drug Candidate for Alzheimer’s Disease via Fyn Tyrosine Kinase Inhibition

Bhat, S. S., Kulkarni, S. R., Uttarkar, A., & Niranjan, V. (2024) · Molecular Biotechnology · doi:10.1007/s12033-024-01236-0 · Cited by 4 · see citing works

Charge distribution and helicity tune the binding of septin's amphipathic helix domain to membranes

Edelmaier, C. J., Klawa, S. J., Mofidi, S. M., Wang, Q., Bhonge, S., Vogt, E. J. D., Curtis, B. N., Shi, W., Hanson, S. M., Klotsa, D., et al. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2025.03.008 · Cited by 4 · see citing works

Ligand trapping as a key mechanism governing drug residence time, binding affinity, and potency

Spassov, D. S. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104694 · Cited by 3 · see citing works

To Bind or Not to Bind? A Comprehensive Characterization of TIR1 and Auxins Using Consensus In Silico Approaches

Prieto-Martínez, F. D., Mendoza-Cañas, J., & Martínez‐Mayorga, K. (2024) · Computation · doi:10.3390/computation12050094 · Cited by 3 · see citing works

Identification of potential inhibitors for MAP4K4 in glaucoma using meta-dynamics-based dissociation free energy calculation

Rustagi, V., Rameshwari, R., & Singh, I. K. (2024) · Brain Research · doi:10.1016/j.brainres.2024.149300 · Cited by 3 · see citing works

Discovery of a potent ROR1 inhibitor using μs-scale MD simulations, wt-metadynamics, and absolute binding free energy calculations

Gupta, S. R. R., Rameshwari, R., & Singh, I. K. (2025) · Journal of Molecular Modeling · doi:10.1007/s00894-025-06508-3 · Cited by 2 · see citing works

Dynamic claw of dengue protease unveils druggability potential with high affinity allosteric inhibitors

Bhunia, M., Misra, R., Maity, A., Mohid, S. A., Kundu, S., Bhunia, A., Maiti, N. C., & Pal, U. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf276 · Cited by 2 · see citing works

Chemically tuned radiolabeling and membrane affinity in Drug-Functionalized carbon nanotube nanocarriers

Abdollahi, S., Raissi, ‪., & Farzad, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-30308-7 · Cited by 2 · see citing works

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.

Das, M., & Venkatramani, R. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00744 · Cited by 1 · see citing works

Integrating the knowledge of traditional medicine to identify potential anti-viral drug against Human Norovirus (HNV); a molecular docking, simulation and network pharmacology analysis

Fakola, E. G., Sarkar, A., Uttarkar, A., Bhattacharya, S., Choudhury, S., Ghosh, S., Niranjan, V., Das, S., & Goswami, P. (2025) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2025.100090 · Cited by 2 · see citing works

Drug Discovery by Automated Adaptation of Chemical Structure and Identity.

Patel, L. A., Chau, P., Debesai, S., Darwin, L., & Neale, C. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01271 · Cited by 1 · see citing works

Absolute binding energy calculation supported with metadynamic simulation and molecular dynamics improves inhibitors identification against GSK3-β for glaucoma treatment

Rustagi, V., Rameshwari, R., Singh, I. K., & Gupta, S. R. R. (2025) · Molecular Simulation · doi:10.1080/08927022.2025.2497288 · Cited by 1 · see citing works

Free Energy and Flexibility Analysis of Autoinhibited Human BRAF

Tempkin, J. O. B., Aydin, F., Essiz, S. G., Yang, Y., Carpenter, T. S., Durrant, D. E., Morrison, D. K., Ingólfsson, H. I., Streitz, F. H., Nissely, D. V., et al. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c03126 · Cited by 1 · see citing works

In Vitro and In Vivo Effects of Resveratrol on Rat Hepatic CYP1A2

Hernández-Ojeda, S. L., López‐Arellano, R., Contreras-Ochoa, C. O., Hernández‐Patlan, D., Camacho‐Carranza, R., Romo‐Mancillas, A., Aguilera-Durán, G., Hernández-Gallardo, C. A., Cárdenas-Ávila, R. I., Govezensky, T., et al. (2025) · Pharmaceuticals · doi:10.3390/ph18111633 · Cited by 1 · see citing works

Contact Parallel Cascade Selection Molecular Dynamics (cPaCS-MD) for Accurate In Silico Prediction of Peptide Binding Free Energy

Prypoten, V., Norton, R. S., & Chalmers, D. K. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02118 · Cited by 1 · see citing works

Design of Novel Imidazopyrazine Derivative against Breast Cancer via Targeted NPY1R Antagonist

Niranjan, V., Vibha, R., Philip, S., Uttarkar, A., Kusanur, R. A., & Kumar, J. (2023) · Anti-Cancer Agents in Medicinal Chemistry · doi:10.2174/1871520623666230505100031 · Cited by 1 · see citing works

Review: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R0/PR1

Wang, J., Bhattarai, A., Do, H. N., et al. (2022) · QRB Discovery · doi:10.1017/qrd.2022.11.pr1

Review: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R0/PR2

Wang, J., Bhattarai, A., Do, H. N., et al. (2022) · QRB Discovery · doi:10.1017/qrd.2022.11.pr2

Recommendation: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R1/PR4

Wang, J., Bhattarai, A., Do, H. N., et al. (2022) · QRB Discovery · doi:10.1017/qrd.2022.11.pr4

Recommendation: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R0/PR3

Wang, J., Bhattarai, A., Do, H. N., et al. (2022) · QRB Discovery · doi:10.1017/qrd.2022.11.pr3

Bias-Deletion Metadynamics Revealing Volume–Rotation Coupling Mechanisms in Metal–Organic Frameworks

Stracke, K., & Evans, J. D. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00051

Coarse-Grained Simulations and Biological Evaluations of Polymer Nanoparticles for Drug Encapsulation and Controlled Release

Gao, P. (2023) · doi:10.70675/c050b2bbzdb11z4a27zbdcdzea50700fbb44

Bases moléculaire de l'interaction protéine-protéine lors de la synaptogenèse

de Lacam, E. G. C. (2023) · theses.fr (ABES) · doi:10.70675/6f0af6e8z43cdz4f3czb462z86c0726d15b4

Modeling and Standard Binding Free Energy Calculations of Complex Biological Objects

Blazhynska, M. (2023) · theses.fr (ABES) · doi:10.70675/48e8ac59z3fecz4841za036z40e6a446b0cc

In silico evaluation of selected triterpenes as potential inhibitors of BRAF and BRAFV600E kinases for cancer treatment

Aguilera-Durán, G., Rivera-Vargas, A., Hernández-Estrada, B., Alvarez-Baltazar, J. M., Loera-García, B. V., Cuevas‐Flores, M. D. R., & Romo‐Mancillas, A. (2026) · Journal of Molecular Modeling · doi:10.1007/s00894-026-06729-0

Understanding solid–liquid phase transition in Titanium from well-tempered metadynamics simulations

Hazarika, M. P., Prasad, M., Tripathi, A., & Chakraborty, S. N. (2026) · Chemical Physics Letters · doi:10.1016/j.cplett.2026.143009

Direct determination of the nucleation free-energy surface by large-scale metadynamics simulations

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ModBind dG : A simulation-based absolute predictor of free energy of binding based on population reweighting

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GroScore: Automated Scoring of Protein–Protein Interactions Using Bidirectional Steered Molecular Dynamics

Perthold, J. W., Petrov, D., & Oostenbrink, C. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01771

Distal amino-acid substitutions contribute to HIV protease inhibitor resistance as directly as proximal amino-acid substitutions

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Ligand binding free-energy calculations with funnel metadynamics

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Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations

Fu, H., Chen, H., Blazhynska, M., de Lacam, E. G. C., Szczepaniak, F., Pavlova, À., Shao, X., Gumbart, J. C., Dehez, F., Roux, B., et al. (2022) · Nature Protocols · doi:10.1038/s41596-021-00676-1 · Cited by 181 · see citing works

Assessing Metadynamics and Docking for Absolute Binding Free Energy Calculations Using Severe Acute Respiratory Syndrome Coronavirus 2 Main Protease Inhibitors

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Free-Energy-Based Methods for Binding Profile Determination in a Congeneric Series of CDK2 Inhibitors

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Funnel metadynamics as accurate binding free-energy method

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