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Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets

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Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM)

Xia, K., Opron, K., & Wei, G.-W. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4936132 · Cited by 43 · see citing works

Temperature artifacts in protein structures bias ligand-binding predictions

Bradford, S., Khoury, L. E., Ge, Y.-H., Osato, M., Mobley, D. L., & Fischer, M. (2021) · Chemical Science · doi:10.1039/d1sc02751d · Cited by 31 · see citing works

Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

Wu, F., Jin, S., Jiang, Y., Jin, X., Tang, B., Niu, Z., Liu, X., Zhang, Q., Zeng, X., & Li, S. Z. (2022) · Advanced Science · doi:10.1002/advs.202203796 · Cited by 42 · see citing works

Water Networks Repopulate Protein–Ligand Interfaces with Temperature

Stachowski, T. R., Vanarotti, M. S., Seetharaman, J., Lopez, K. M., & Fischer, M. M. (2022) · Angewandte Chemie International Edition · doi:10.1002/anie.202112919 · Cited by 41 · see citing works

Computational design of ligand-binding membrane receptors with high selectivity

Feng, X., Ambía, J., Chen, K.-Y. M., Young, M., & Barth, P. (2017) · Nature Chemical Biology · doi:10.1038/nchembio.2371 · Cited by 39 · see citing works

Fragment-Based Discovery and Optimization of Enzyme Inhibitors by Docking of Commercial Chemical Space

Rudling, A., Gustafsson, R., Almlöf, I., Homan, E. J., Scobie, M., Berglund, U. W., Helleday, T., Stenmark, P., & Carlsson, J. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b01006 · Cited by 38 · see citing works

Large-Scale Ligand Perturbations of the Protein Conformational Landscape Reveal State-Specific Interaction Hotspots

Stachowski, T. R., & Fischer, M. M. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00708 · Cited by 35 · see citing works

Equivariant Flexible Modeling of the Protein–Ligand Binding Pose with Geometric Deep Learning

Dong, T., Yang, Z., Zhou, J., & Chen, C. Y. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00273 · Cited by 35 · see citing works

Mimicking Strategy for Protein–Protein Interaction Inhibitor Discovery by Virtual Screening

Wu, K., Lei, P.-M., Liu, H., Chun, W., Leung, C., & Ma, D. (2019) · Molecules · doi:10.3390/molecules24244428 · Cited by 35 · see citing works

Small-Molecule Allosteric Modulators of the Protein Kinase PDK1 from Structure-Based Docking

Rettenmaier, T. J., Fan, H., Karpiak, J. D., Doak, A. K., Šali, A., Shoichet, B. K., & Wells, J. A. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01216 · Cited by 35 · see citing works

The impact of crystallization conditions on structure‐based drug design: A case study on the methylene blue/acetylcholinesterase complex

Dym, O., Song, W., Felder, C. E., Roth, E., Shnyrov, V. L., Ashani, Y., Xu, Y., Joosten, R. P., Weiner, L. M., Sussman, J. L., et al. (2016) · Protein Science · doi:10.1002/pro.2923 · Cited by 33 · see citing works

Structure-Based Discovery of Lipoteichoic Acid Synthase Inhibitors

Wezen, X. C., Chandran, A., Eapen, R., Waters, E. M., Bricio-Moreno, L., Tosi, T., Dolan, S. K., Millership, C., Kadioglu, A., Gründling, A., et al. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00300 · Cited by 34 · see citing works

Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R grand challenge

Mey, A. S. J. S., Juárez‐Jiménez, J., Hennessy, A., & Michel, J. (2016) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2016.07.044 · Cited by 34 · see citing works

AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles

Bowman, G. R. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-102423-011435 · Cited by 35 · see citing works

Systematic exploration of multiple drug binding sites

Bálint, M., Jeszenői, N., Horváth, I., van der Spoel, D., & Hetényi, C. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0255-6 · Cited by 33 · see citing works

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 · see citing works

The Perturbed Free‐Energy Landscape: Linking Ligand Binding to Biomolecular Folding

Abdelsattar, A. S., Mansour, Y., & Aboul‐ela, F. (2020) · ChemBioChem · doi:10.1002/cbic.202000695 · Cited by 30 · see citing works

Cell phenotypes can be predicted from propensities of protein conformations

Nussinov, R., Liu, Y., Zhang, W., & Jang, H. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102722 · Cited by 30 · see citing works

Protein Flexibility in Docking-Based Virtual Screening: Discovery of Novel Lymphoid-Specific Tyrosine Phosphatase Inhibitors Using Multiple Crystal Structures

Hou, X., Li, K., Yu, X., Sun, J., & Fang, H. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00344 · Cited by 30 · see citing works

Discovery of new MurA inhibitors using induced-fit simulation and docking

Rožman, K., Lešnik, S., Brus, B., Hrast, M., Sova, M., Patin, D., Barreteau, H., Konc, J., Janežič, D., & Gobec, S. (2017) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2016.12.082 · Cited by 29 · see citing works

Cosolvent-Based Molecular Dynamics for Ensemble Docking: Practical Method for Generating Druggable Protein Conformations

Uehara, S., & Tanaka, S. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00791 · Cited by 28 · see citing works

A dynamic Asp–Arg interaction is essential for catalysis in microsomal prostaglandin E 2 synthase

Brock, J., Hámberg, M., Balagunaseelan, N., Goodman, M., Morgenstern, R., Strandback, E., Samuelsson, B. O., Rinaldo-Matthis, A., & Haeggström, J. Z. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1522891113 · Cited by 27 · see citing works

Exploration of structure-activity relationships for the SARS-CoV-2 macrodomain from shape-based fragment linking and active learning

Correy, G. J., Rachman, M. M., Togo, T., Gahbauer, S., Doruk, Y. U., Stevens, M. G., Jaishankar, P., Kelley, B. P., Goldman, B. B., Schmidt, M., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads7187 · Cited by 26 · see citing works

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs

Alhumaid, N. K., & Tawfik, E. A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251810139 · Cited by 23 · see citing works

Rare Diseases: Drug Discovery and Informatics Resource

Zhao, M., & Wei, D. (2017) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-017-0270-3 · Cited by 25 · see citing works

Toxicological effects of bisphenol A exposure-induced cancer cells migration via activating directly integrin β1

Jia, B., Shi, T., Li, Z., Shan, S., Ji, P., & Li, Z. (2018) · Chemosphere · doi:10.1016/j.chemosphere.2018.12.120 · Cited by 25 · see citing works

X-Entropy: A Parallelized Kernel Density Estimator with Automated Bandwidth Selection to Calculate Entropy

Kraml, J., Hofer, F., Quoika, P. K., Kamenik, A. S., & Liedl, K. R. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01375 · Cited by 24 · see citing works

Ensemble Docking for Intrinsically Disordered Proteins

Dhar, A., Sisk, T. R., & Robustelli, P. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00370 · Cited by 23 · see citing works

Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries

Xu, D. D., & Meroueh, S. O. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00709 · Cited by 23 · see citing works

Evaluation of the novel algorithm of flexible ligand docking with moveable target-protein atoms

Sulimov, A. V., Zheltkov, D. A., Oferkin, I. V., Kutov, D. C., Katkova, E. V., Tyrtyshnikov, E. E., & Sulimov, V. B. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2017.02.004 · Cited by 23 · see citing works

Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors

Martinez-Rosell, G., Harvey, M. J., & Fabritiis, G. D. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00625 · Cited by 23 · see citing works

Dynophore-Based Approach in Virtual Screening: A Case of Human DNA Topoisomerase IIα

Janežič, M., Valjavec, K., Bergant, K., Herlah, B., Ogris, I., Kozorog, M., Podobnik, M., Grdadolnik, S. G., Wolber, G., & Perdih, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222413474 · Cited by 23 · see citing works

Interaction between tigecycline and human serum albumin in aqueous solution

Stojanović, S. D., Јаnkovic, S., Matović, Z. D., Jakovljević, I. Ž., & Jelić, R. (2014) · Monatshefte für Chemie - Chemical Monthly · doi:10.1007/s00706-014-1330-6 · Cited by 21 · see citing works

Markov models of the apo-MDM2 lid region reveal diffuse yet two-state binding dynamics and receptor poses for computational docking

Mukherjee, S., Pantelopulos, G. A., & Voelz, V. A. (2016) · Scientific Reports · doi:10.1038/srep31631 · Cited by 20 · see citing works

FLEXR : automated multi-conformer model building using electron-density map sampling

Stachowski, T. R., & Fischer, M. M. (2023) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798323002498 · Cited by 20 · see citing works

Localization of Millisecond Dynamics: Dihedral Entropy from Accelerated MD

Kamenik, A. S., Kahler, U., Fuchs, J. E., & Liedl, K. R. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00231 · Cited by 20 · see citing works

Virtual fragment screening for DNA repair inhibitors in vast chemical space

Luttens, A., Vo, D. D., Scaletti, E. R., Wiita, E., Almlöf, I., Wallner, O. A., Davies, J. R., Košenina, S. P., Meng, L., Long, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56893-9 · Cited by 19 · see citing works

DOCK 6: Incorporating hierarchical traversal through precomputed ligand conformations to enable large‐scale docking

Balius, T. E., Tan, Y.-H., & Chakrabarti, M. (2023) · Journal of Computational Chemistry · doi:10.1002/jcc.27218 · Cited by 19 · see citing works

Predicting how drug molecules bind to their protein targets

Rachman, M. M., Barril, X., & Hubbard, R. E. (2018) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2018.07.001 · Cited by 19 · see citing works

Microfluidic deep mutational scanning of the human executioner caspases reveals differences in structure and regulation

Roychowdhury, H., & Romero, P. A. (2022) · Cell Death Discovery · doi:10.1038/s41420-021-00799-0 · Cited by 18 · see citing works

Toward physics‐based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

Meller, A., Kelly, D. G., Smith, L. G., & Bowman, G. R. (2024) · Protein Science · doi:10.1002/pro.4902 · Cited by 17 · see citing works

On the Power and Challenges of Atomistic Molecular Dynamics to Investigate RNA Molecules

Muscat, S., Martino, G., Manigrasso, J., Marcia, M., & Vivo, M. D. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00773 · Cited by 18 · see citing works

A New Scoring Function for Molecular Docking Based on AutoDock and AutoDock Vina

Tanchuk, V. Y., Tanin, V. O., Vovk, A. I., & Poda, G. I. (2015) · Current Drug Discovery Technologies · doi:10.2174/1570163812666150825110208 · Cited by 18 · see citing works

Unlocking the application potential of AlphaFold3-like approaches in virtual screening

Shen, C., Zhang, X., Gu, S., Zhang, O., Wang, Q., Du, G., Zhao, Y., Jiang, L., Pan, P., Kang, Y., et al. (2025) · Chemical Science · doi:10.1039/d5sc06481c · Cited by 17 · see citing works

Measuring thermodynamic preferences to form non-native conformations in nucleic acids using ultraviolet melting

Rangadurai, A., Shi, H., Xu, Y., Liu, B., Assi, H. A., Boom, J. D., Zhou, H., Kimsey, I. J., & Al‐Hashimi, H. M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2112496119 · Cited by 19 · see citing works

Molecular Design, Synthesis, Computational Screening, Antimicrobial Evaluation and Molecular Docking Study of Acetylinic Isatin Hybrids

Singh, G., Arora, A., Singh, A., Kalra, P., Rani, S., Singh, K., Maurya, I. K., & Mandal, R. S. (2018) · ChemistrySelect · doi:10.1002/slct.201703051 · Cited by 17 · see citing works

Hic Sunt Dracones: Molecular Docking in Uncharted Territories with Structures from AlphaFold2 and RoseTTAfold

Kersten, C., Clower, S., & Barthels, F. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01400 · Cited by 16 · see citing works

PIDiff: Physics informed diffusion model for protein pocket-specific 3D molecular generation

Choi, S., Seo, S., Kim, B. J., Park, C., & Park, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108865 · Cited by 16 · see citing works

Pushed to extremes: distinct effects of high temperature versus pressure on the structure of STEP

Guerrero, L., Ebrahim, A., Riley, B. T., Kim, M., Huang, Q., Finke, A. D., & Keedy, D. A. (2024) · Communications Biology · doi:10.1038/s42003-023-05609-0 · Cited by 15 · see citing works

Dynamics-based drug discovery by time-resolved cryo-EM

Mao, Y. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103001 · Cited by 15 · see citing works

Integrating microsecond timescale classical and biased molecular dynamics simulations to screen potential molecules for BRD4-BD1

Bhardwaj, V. K., Das, P., & Purohit, R. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.113061 · Cited by 14 · see citing works

Computational Strategy for Bound State Structure Prediction in Structure-Based Virtual Screening: A Case Study of Protein Tyrosine Phosphatase Receptor Type O Inhibitors

Hou, X., Rooklin, D. W., Yang, D., Liang, X., Li, K., Lu, J., Wang, C., Xiao, P., Zhang, Y., Sun, J., et al. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00548 · Cited by 14 · see citing works

Discovery of Novel Pim-1 Kinase Inhibitors with a Flexible-Receptor Docking Protocol

Li, G., Li, W., Xie, Y., Wan, X., Zheng, G., Huang, N., & Zhou, Y. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00494 · Cited by 13 · see citing works

Unfreezing structural biology for drug discovery

Stachowski, T. R., & Fischer, M. M. (2025) · Nature Chemical Biology · doi:10.1038/s41589-025-02047-3 · Cited by 12 · see citing works

Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents

Ng, H. W., Leggett, C. S., Sakkiah, S., Pan, B., Ye, H., Wu, L., Selvaraj, C., Tong, W., & Hong, H. (2018) · Oncotarget · doi:10.18632/oncotarget.24458 · Cited by 12 · see citing works

Allosteric Inhibition of Bacterial Targets: An Opportunity for Discovery of Novel Antibacterial Classes

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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 · see citing works

The protein flexibility in receptor-ligand docking simulations

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Managing protein flexibility in docking and its applications

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Flexible ligand docking to multiple receptor conformations: a practical alternative

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Computational strategies to include protein flexibility in Ligand Docking and Virtual Screening

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