Development and testing of a general amber force field

Computer Simulation of Liquids

Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945

The Amber biomolecular simulation programs

Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20290 · Cited by 9,535

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

Macromolecular modeling and design in Rosetta: recent methods and frameworks

Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0848-2 · Cited by 888

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849

Machine learning unifies the modeling of materials and molecules

Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances · doi:10.1126/sciadv.1701816 · Cited by 764

Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set.

Sun, H., Li, Y., Tian, S., Xu, L., & Hou, T. (2014) · Phys. Chem. Chem. Phys. · doi:10.1039/c4cp01388c · Cited by 738

Efficient and long-lived room-temperature organic phosphorescence: theoretical descriptors for molecular designs

Ma, H., Peng, Q., An, Z., Huang, W., & Shuai, Z. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.8b11224 · Cited by 613

Recycling of multilayer plastic packaging materials by solvent-targeted recovery and precipitation

Walker, T. W., Frelka, N., Shen, Z., Chew, A. K., Banick, J., Grey, S., Kim, M. S., Dumesic, J. A., Van Lehn, R. C., & Huber, G. W. (2020) · Science Advances · doi:10.1126/sciadv.aba7599 · Cited by 442

Accurate calculation of the absolute free energy of binding for drug molecules

Aldeghi, M., Heifetz, A., Bodkin, M. J., Knapp, S., & Biggin, P. C. (2016) · Chemical Science · doi:10.1039/c5sc02678d · Cited by 347

Activation pathway of Src kinase reveals intermediate states as targets for drug design

Shukla, D., Meng, Y., Roux, B., & Pande, V. S. (2014) · Nature Communications · doi:10.1038/ncomms4397 · Cited by 333

On the use of orientational restraints and symmetry corrections in alchemical free energy calculations

Mobley, D. L., Chodera, J. D., & Dill, K. A. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2221683 · Cited by 316

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289

Characterization of Lipid-Protein Interactions and Lipid-Mediated Modulation of Membrane Protein Function through Molecular Simulation

Muller, M. P., Jiang, T., Sun, C., Lihan, M., Pant, S., Mahinthichaichan, P., Trifan, A., & Tajkhorshid, E. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00608 · Cited by 282

Protein sequences bound to mineral surfaces persist into deep time

Demarchi, B., Hall, S., Roncal-Herrero, T., Freeman, C. L., Woolley, J., Crisp, M. K., Wilson, J., Fotakis, A., Fischer, R., Kessler, B. M., et al. (2016) · eLife · doi:10.7554/elife.17092 · Cited by 279

Martini coarse-grained force field: Extension to RNA

Uusitalo, J. J., Ingólfsson, H. I., Marrink, S. J., & Faustino, I. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.05.043 · Cited by 259

Two decades of Martini: Better beads, broader scope

Marrink, S. J., Monticelli, L., Melo, M. N., Alessandri, R., Tieleman, D. P., & Souza, P. C. T. (2023) · WIREs Computational Molecular Science · doi:10.1002/wcms.1620 · Cited by 255

The Properties of Water: Insights from Quantum Simulations

Paesani, F., & Voth, G. A. (2009) · The Journal of Physical Chemistry B · doi:10.1021/jp810590c · Cited by 256

Stereoselective amino acid synthesis by synergistic photoredox-pyridoxal radical biocatalysis

Cheng, L., Li, D., Mai, B. K., Bo, Z., Cheng, L., Liu, P., & Yang, Y. (2023) · Science · doi:10.1126/science.adg2420 · Cited by 253

Using AMBER18 for Relative Free Energy Calculations

Song, L. F., Lee, T.-S., Zhu, C., York, D. M., & Merz, K. M. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00105 · Cited by 234

Assessment of Binding Affinity via Alchemical Free-Energy Calculations

Kuhn, M., Firth-Clark, S., Tosco, P., Mey, A. S. J. S., Mackey, M., & Michel, J. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00165 · Cited by 222

When PERK inhibitors turn out to be new potent RIPK1 inhibitors: critical issues on the specificity and use of GSK2606414 and GSK2656157

Rojas-Rivera, D., Delvaeye, T., Roelandt, R., Nerinckx, W., Augustyns, K., Vandenabeele, P., & Bertrand, M. J. M. (2017) · Cell Death & Differentiation · doi:10.1038/cdd.2017.58 · Cited by 206

Mitochondrial Targeting of Vitamin E Succinate Enhances Its Pro-apoptotic and Anti-cancer Activity via Mitochondrial Complex II

Dong, L.-F., Jameson, V. J. A., Tilly, D., Cerny, J., Mahdavian, E., Marín-Hernández, A., Hernández-Esquivel, L., Rodríguez-Enríquez, S., Stursa, J., Witting, P. K., et al. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.186643 · Cited by 200

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

Janet, J. P., Chan, L., & Kulik, H. J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00170 · Cited by 200

Predicting electronic structure properties of transition metal complexes with neural networks

Janet, J. P., & Kulik, H. J. (2017) · Chemical Science · doi:10.1039/c7sc01247k · Cited by 199

Unleashing the power of generative AI in drug discovery

Gangwal, A., & Lavecchia, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103992 · Cited by 195

Selective PP2A enhancement through biased heterotrimer stabilization

Leonard, D., Huang, W., Izadmehr, S., O’Connor, C. M., Wiredja, D. D., Wang, Z., Zaware, N., Chen, Y., Schlatzer, D. M., Kiselar, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.038 · Cited by 176

Small molecule inhibitors of ezrin inhibit the invasive phenotype of osteosarcoma cells

Bulut, G., Hong, S.-H., Chen, K., Beauchamp, E. M., Rahim, S., Kosturko, G. W., Glasgow, E., Dakshanamurthy, S., Lee, H.-S., Daar, I., et al. (2012) · Oncogene · doi:10.1038/onc.2011.245 · Cited by 179

Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations

Aldeghi, M., Heifetz, A., Bodkin, M. J., Knapp, S., & Biggin, P. C. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.6b11467 · Cited by 177

Adsorption, Structure, and Dynamics of Short- and Long-Chain PFAS Molecules in Kaolinite: Molecular-Level Insights

Loganathan, N., & Wilson, A. K. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.2c01054 · Cited by 177

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

The radical mechanism of biological methane synthesis by methyl-coenzyme M reductase

Wongnate, T., Sliwa, D., Ginovska, B., Smith, D., Wolf, M. W., Lehnert, N., Raugei, S., & Ragsdale, S. W. (2016) · Science · doi:10.1126/science.aaf0616 · Cited by 170

Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks

Wu, K., & Wei, G.-W. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00558 · Cited by 160

DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs

Li, F., Hu, Q., Zhang, X., Sun, R., Liu, Z., Wu, S., Tian, S., Ma, X., Dai, Z., Yang, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34807-3 · Cited by 159

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

Vandermause, J., Xie, Y., Lim, J. S., Owen, C. J., & Kozinsky, B. (2022) · Nature Communications · doi:10.1038/s41467-022-32294-0 · Cited by 147

Predicting Relative Protein Affinity of Novel Per- and Polyfluoroalkyl Substances (PFASs) by An Efficient Molecular Dynamics Approach

Cheng, W., & Ng, C. A. (2018) · Environmental Science & Technology · doi:10.1021/acs.est.8b01268 · Cited by 141

A molecular mechanism for transthyretin amyloidogenesis

Yee, A. W., Aldeghi, M., Blakeley, M. P., Ostermann, A., Mas, P. J., Moulin, M., de Sanctis, D., Bowler, M. W., Mueller-Dieckmann, C., Mitchell, E. P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08609-z · Cited by 139

Computational tools to model halogen bonds in medicinal chemistry.

Ford, M. C., & Ho, P. S. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00997 · Cited by 143

A hydrophobic ratchet entrenches molecular complexes

Hochberg, G. K. A., Liu, Y., Marklund, E. G., Metzger, B. P. H., Laganowsky, A., & Thornton, J. W. (2020) · Nature · doi:10.1038/s41586-020-3021-2 · Cited by 134

Cryo-EM structure of the agonist-bound Hsp90-XAP2-AHR cytosolic complex

Gruszczyk, J., Grandvuillemin, L., Lai-Kee-Him, J., Paloni, M., Savva, C. G., Germain, P., Grimaldi, M., Boulahtouf, A., Kwong, H.-S., Bous, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34773-w · Cited by 137

Characterization of Histone Deacetylase 8 (HDAC8) Selective Inhibition Reveals Specific Active Site Structural and Functional Determinants

Marek, M., Shaik, T. B., Heimburg, T., Chakrabarti, A., Lancelot, J., Ramos-Morales, E., Da Veiga, C., Kalinin, D., Melesina, J., Robaa, D., et al. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b01087 · Cited by 122

CSM-lig: a web server for assessing and comparing protein-small molecule affinities

Pires, D. E. V., & Ascher, D. B. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw390 · Cited by 115

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Janet, J. P., Liu, F., Nandy, A., Duan, C., Yang, T., Lin, S., & Kulik, H. J. (2019) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.9b00109 · Cited by 114

Structural basis of second-generation HIV integrase inhibitor action and viral resistance

Cook, N. J., Li, W., Berta, D., Badaoui, M., Ballandras-Colas, A., Nans, A., Kotecha, A., Rosta, E., Engelman, A. N., & Cherepanov, P. (2020) · Science · doi:10.1126/science.aay4919 · Cited by 108

Modeling the binding of diverse ligands within the Ah receptor ligand binding domain

Giani Tagliabue, S., Faber, S. C., Motta, S., Denison, M. S., & Bonati, L. (2019) · Scientific Reports · doi:10.1038/s41598-019-47138-z · Cited by 106

Structural conservation of antibiotic interaction with ribosomes

Paternoga, H., Crowe-McAuliffe, C., Bock, L. V., Koller, T. O., Morici, M., Beckert, B., Myasnikov, A. G., Grubmüller, H., Nováček, J., & Wilson, D. N. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01047-y · Cited by 94

A structural mechanism for directing corepressor-selective inverse agonism of PPARγ

Brust, R., Shang, J., Fuhrmann, J., Mosure, S. A., Bass, J., Cano, A., Heidari, Z., Chrisman, I. M., Nemetchek, M. D., Blayo, A.-L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07133-w · Cited by 94

Untitled work

Zhang (2017) · doi:10.3390/molecules23020228 · Cited by 93

Different positron emission tomography tau tracers bind to multiple binding sites on the tau fibril: insight from computational modeling

Murugan, N. A., Nordberg, A., & Ågren, H. (2018) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.8b00093 · Cited by 92

Old drug new use - Amoxapine and its metabolites as potent bacterial β-glucuronidase inhibitors for alleviating cancer drug toxicity

Kong, R., Liu, T., Zhu, X., Ahmad, S., Williams, A. L., Phan, A. T., Zhao, H., Scott, J. E., Yeh, L.-A., & Wong, S. T. C. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-14-0395 · Cited by 90

Rational optimization of a transcription factor activation domain inhibitor

Basu, S., Martínez-Cristóbal, P., Frigolé-Vivas, M., Pesarrodona, M., Lewis, M., Szulc, E., Bañuelos, C. A., Sánchez-Zarzalejo, C., Bielskutė, S., Zhu, J., et al. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01159-5 · Cited by 83

Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

Buehler, M. J. (2024) · ACS Engineering Au · doi:10.1021/acsengineeringau.3c00058 · Cited by 82

Predicting glycan structure from tandem mass spectrometry via deep learning

Urban, J., Jin, C., Thomsson, K. A., Karlsson, N. G., Ives, C. M., Fadda, E., & Bojar, D. (2024) · Nature Methods · doi:10.1038/s41592-024-02314-6 · Cited by 79

Ribonuclease recruitment using a small molecule reduced c9ALS/FTD r(G 4 C 2 ) repeat expansion in vitro and in vivo ALS models

Bush, J. A., Aikawa, H., Fuerst, R., Li, Y., Ursu, A., Meyer, S. M., Benhamou, R. I., Chen, J. L., Khan, T., Wagner-Griffin, S., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abd5991 · Cited by 77

Update to the General Amber Force Field for Small Solutes with an Emphasis on Free Energies of Hydration

Jämbeck, J. P. M., & Lyubartsev, A. P. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp4111234 · Cited by 73

Metadynamics in the conformational space nonlinearly dimensionally reduced by Isomap

Spiwok, V., & Králová, B. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3660208 · Cited by 70

Interactions of the Calcite {10.4} Surface with Organic Compounds: Structure and Behaviour at Mineral – Organic Interfaces

Hakim, S. S., Olsson, M. H. M., Sørensen, H. O., Bovet, N., Bohr, J., Feidenhans’l, R., & Stipp, S. L. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-06977-4 · Cited by 66

Structural basis for the ligand-dependent activation of heterodimeric AHR-ARNT complex

Diao, X., Shang, Q., Guo, M., Huang, Y., Zhang, M., Chen, X., Liang, Y., Sun, X., Zhou, F., Zhuang, J., et al. (2025) · Nature communications · doi:10.1038/s41467-025-56574-7 · Cited by 72

iCn3D: From Web-Based 3D Viewer to Structural Analysis Tool in Batch Mode

Wang, J., Youkharibache, P., Marchler-Bauer, A., Lanczycki, C., Zhang, D., Lu, S., Madej, T., Marchler, G. H., Cheng, T., Chong, L. C., et al. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.831740 · Cited by 65

New deep eutectic solvent based superparamagnetic nanofluid for determination of perfluoroalkyl substances in edible oils

Fan, C., Wang, H., Liu, Y., & Cao, X. (2021) · Talanta · doi:10.1016/j.talanta.2021.122214 · Cited by 58

HNF4A and HNF1A exhibit tissue specific target gene regulation in pancreatic beta cells and hepatocytes

Ng, N. H. J., Ghosh, S., Bok, C. M., Ching, C., Low, B. S. J., Chen, J. T., Lim, E., Miserendino, M. C., Tan, Y. S., Hoon, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48647-w · Cited by 57

A multiscale approach to predicting affinity changes in protein-protein interfaces: Multiscale Method for Predicting ΔΔG

Dourado, D. F. A. R., & Flores, S. C. (2014) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24634 · Cited by 56

A Transferable Nonbonded Pairwise Force Field to Model Zinc Interactions in Metalloproteins

Wu, R., Lu, Z., Cao, Z., & Zhang, Y. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct100525r · Cited by 53

Insights from incorporating quantum computing into drug design workflows

Lau, B., Emani, P. S., Chapman, J., Yao, L., Lam, T., Merrill, P., Warrell, J., Gerstein, M. B., & Lam, H. Y. K. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac789 · Cited by 54

Importance of polar solvation and configurational entropy for design of antiretroviral drugs targeting HIV-1 protease.

Kar, P., Lipowsky, R., & Knecht, V. (2013) · The Journal of Physical Chemistry B · doi:10.1021/jp3085292 · Cited by 53

Insights into the missing apiosylation step in flavonoid apiosides biosynthesis of Leguminosae plants

Wang, H.-T., Wang, Z.-L., Chen, K., Yao, M.-J., Zhang, M., Wang, R.-S., Zhang, J.-H., Ågren, H., Li, F.-D., Li, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42393-1 · Cited by 53

Identification of Small Molecule Translesion Synthesis Inhibitors That Target the Rev1-CT/RIR Protein-Protein Interaction

Sail, V., Rizzo, A. A., Chatterjee, N., Dash, R. C., Ozen, Z., Walker, G. C., Korzhnev, D. M., & Hadden, M. K. (2017) · ACS Chemical Biology · doi:10.1021/acschembio.6b01144 · Cited by 52

1′-Ribose cyano substitution allows Remdesivir to effectively inhibit nucleotide addition and proofreading during SARS-CoV-2 viral RNA replication

Zhang, L., Zhang, D., Wang, X., Yuan, C., Li, Y., Jia, X., Gao, X., Yen, H.-L., Cheung, P. P.-H., & Huang, X. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp05948j · Cited by 52

Experimental and molecular dynamics simulation study of specific ion effect on the graphene oxide surface and investigation of the influence on reactive extraction of model dye molecule at water–organic interface

Borthakur, P., Boruah, P. K., Hussain, N., Sharma, B., Das, M. R., Matić, S., Řeha, D., & Minofar, B. (2016) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.6b02787 · Cited by 49

Identification of oleoylethanolamide as an endogenous ligand for HIF-3α

Diao, X., Ye, F., Zhang, M., Ren, X., Tian, X., Lu, J., Sun, X., Hou, Z., Chen, X., Li, F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30338-z · Cited by 51

Molecular MRI quantification of extracellular aldehyde pairs for early detection of liver fibrogenesis and response to treatment

Ning, Y., Zhou, I. Y., Roberts, J. D., Rotile, N. J., Akam, E., Barrett, S. C., Sojoodi, M., Barr, M. N., Punshon, T., Pantazopoulos, P., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abq6297 · Cited by 47

Stability Mechanism of Menthol and Fatty Acid Based Hydrophobic Eutectic Solvents: Insights from Nonbonded Interactions

Paul, N., Harish, G., & Banerjee, T. (2023) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.2c06137 · Cited by 51

Characterization of small molecule binding. I. Accurate Identification of Strong Inhibitors in Virtual Screening.

Ding, B., Wang, J., Li, N., & Wang, W. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300508m · Cited by 45

Deciphering Dimerization Modes of PAS Domains: Computational and Experimental Analyses of the AhR:ARNT Complex Reveal New Insights Into the Mechanisms of AhR Transformation

Corrada, D., Soshilov, A. A., Denison, M. S., & Bonati, L. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004981 · Cited by 43

Adsorption of Ethanol and Water on Calcite: Dependence on Surface Geometry and Effect on Surface Behavior

Keller, K. S., Olsson, M. H. M., Yang, M., & Stipp, S. L. S. (2015) · Langmuir · doi:10.1021/la504319z · Cited by 39

Structural Insights into the Activation of Human Aryl Hydrocarbon Receptor by the Environmental Contaminant Benzo[a]pyrene and Structurally Related Compounds

Kwong, H.-S., Paloni, M., Grandvuillemin, L., Sirounian, S., Ancelin, A., Lai-Kee-Him, J., Grimaldi, M., Carivenc, C., Lancey, C., Ragan, T. J., et al. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168411 · Cited by 40

Biological and Computational Evaluation of Resveratrol Inhibitors against Alzheimer's Disease

Koukoulitsa, C., Villalonga-Barber, C., Csonka, R., Alexi, X., Leonis, G., Dellis, D., Hamelink, E., Belda, O., Steele, B. R., Micha-Screttas, M., et al. (2016) · Journal of Enzyme Inhibition and Medicinal Chemistry · doi:10.3109/14756366.2014.1003928 · Cited by 37

Cooperativity between the orthosteric and allosteric ligand binding sites of RORγt

de Vries, R. M. J. M., Meijer, F. A., Doveston, R. G., Leijten-van de Gevel, I. A., & Brunsveld, L. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021287118 · Cited by 38

Ranking power of the SQM/COSMO scoring function on carbonic anhydrase II-inhibitor complexes.

Pecina, A., Brynda, J., Vrzal, L., Gnanasekaran, R., Hořejší, M., Eyrilmez, S. M., Řezáč, J., Lepšík, M., Řezáčová, P., Hobza, P., et al. (2018) · ChemPhysChem · doi:10.1002/cphc.201701104 · Cited by 36

Investigating substrate scope and enantioselectivity of a defluorinase by a stereochemical probe

Wang, J.-B., Ilie, A., Yuan, S., & Reetz, M. T. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.7b06019 · Cited by 36

The Cluster of Hydrophobic Residues Controls the Entrance to the Active Site of Choline Oxidase

Xin, Y., Gadda, G., & Hamelberg, D. (2009) · Biochemistry · doi:10.1021/bi901295a · Cited by 33

Molecular Dynamics Simulations, Solvatochromic Parameters, and Preferential Solvation in Aqueous Solutions of Ethaline, Ethylene Glycol, and Choline Chloride

Aryafard, M., Karimi, A., Harifi-Mood, A. R., & Minofar, B. (2020) · Journal of Chemical & Engineering Data · doi:10.1021/acs.jced.0c00381 · Cited by 29

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

ExEnDiff: An Experiment-Guided Diffusion Model for Protein Conformational Ensemble Generation

Liu, Y., Yu, Z., Lindsay, R. J., Lin, G., Chen, M., Sahoo, A., & Hanson, S. M. (2025) · PRX Life · doi:10.1103/prxlife.3.023013 · Cited by 28

Transient States and Barriers from Molecular Simulations and the Milestoning Theory: Kinetics in Ligand–Protein Recognition and Compound Design

Tang, Z., Chen, S.-H., & Chang, C.-E. A. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b01153 · Cited by 28

Design, synthesis, and in vitro and in vivo characterization of new memantine analogs for Alzheimer’s disease

Turcu, A. L., Companys-Alemany, J., Phillips, M. B., Patel, D. S., Griñán-Ferré, C., Loza, M. I., Brea, J. M., Pérez, B., Soto, D., Sureda, F. X., et al. (2022) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2022.114354 · Cited by 27

Characterization of tryptophan oxidation affecting D1 degradation by FtsH in the photosystem II quality control of chloroplasts

Kato, Y., Kuroda, H., Ozawa, S.-I., Saito, K., Dogra, V., Scholz, M., Zhang, G., de Vitry, C., Ishikita, H., Kim, C., et al. (2023) · eLife · doi:10.7554/elife.88822.3 · Cited by 26

Challenges of docking in large, flexible and promiscuous binding sites

Kotev, M., Soliva, R., & Orozco, M. (2016) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2016.08.010 · Cited by 25

Protein-ligand binding enthalpies from near-millisecond simulations: Analysis of a preorganization paradox

Li, A., & Gilson, M. K. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5027439 · Cited by 24

Extraction and interaction insights for enhanced separation of phenolic compounds from model coal tar using a hydroxyl-functionalized ionic liquid

Xu, D., Wang, S., Zhang, T., Peng, L., Bing, X., Zhang, L., Ma, Y., Gao, J., & Wang, Y. (2022) · Chemical Engineering Research and Design · doi:10.1016/j.cherd.2022.01.004 · Cited by 23

Harnessing artificial intelligence to identify Bufalin as a molecular glue degrader of estrogen receptor alpha

Jiang, S., Liu, K., Jiang, T., Li, H., Wei, X., Wan, X., Zhong, C., Gong, R., Chen, Z., Zou, C., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62288-7 · Cited by 23

MISATO - Machine learning dataset of protein-ligand complexes for structure-based drug discovery

(2023) · doi:10.1101/2023.05.24.542082 · Cited by 18

New scaffolds of natural origin as Integrase–LEDGF/p75 interaction inhibitors: Virtual screening and activity assays

De Luca, L., Morreale, F., Christ, F., Debyser, Z., Ferro, S., & Gitto, R. (2013) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2013.07.025 · Cited by 17

Rational design of 19F NMR labelling sites to probe protein structure and interactions

Streit, J. O., Chan, S. H. S., Daya, S., & Christodoulou, J. (2025) · Nature Communications · doi:10.1038/s41467-025-59105-6 · Cited by 17

DynaMate: Leveraging AI-agents for customized research workflows

Mendible-Barreto, O. A., Díaz-Maldonado, M., Carmona Esteva, F. J., Torres, J. E., Córdova-Figueroa, U. M., & Colón, Y. J. (2025) · Molecular Systems Design & Engineering · doi:10.1039/d5me00062a · Cited by 12

Fisetin as a Blueprint for Senotherapeutic Agents – Elucidating Geroprotective and Senolytic Properties with Molecular Modeling

Spiegel, M. (2025) · Chemistry – A European Journal · doi:10.1002/chem.202403755 · Cited by 12

VirtuousPocketome: a computational tool for screening protein–ligand complexes to identify similar binding sites

Pallante, L., Cannariato, M., Androutsos, L., Zizzi, E. A., Bompotas, A., Hada, X., Grasso, G., Kalogeras, A., Mavroudi, S., Di Benedetto, G., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-56893-7 · Cited by 12

LassoPred: a tool to predict the 3D structure of lasso peptides

Ouyang, X., Ran, X., Xu, H., Al-Abssi, R., Zhao, Y.-L., Link, A. J., & Yang, Z. J. (2025) · Nature Communications · doi:10.1038/s41467-025-60544-4 · Cited by 11

Targeting Tuberculosis: Novel Scaffolds for Inhibiting Cytochrome bd Oxidase

Seitz, C., Ahn, S.-H., Wei, H., Kyte, M., Cook, G. M., Krause, K. L., & McCammon, J. A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00344 · Cited by 11

Chatbot-assisted quantum chemistry for explicitly solvated molecules

(2025) · doi:10.1039/d4sc08677e · Cited by 11