Using AMBER18 for Relative Free Energy Calculations

Song, Lin Frank; Lee, Tai-Sung; Zhu, Chun; York, Darrin M.; Merz, Kenneth M.

doi:10.1021/acs.jcim.9b00105

Cited by 237Computational Drug Discovery MethodsAdvanced Chemical Physics StudiesProtein Structure and Dynamics

Using AMBER18 for Relative Free Energy Calculations

Open access

Song, Lin Frank, Lee, Tai-Sung, Zhu, Chun, York, Darrin M., Merz, Kenneth M.·2019·Journal of Chemical Information and Modeling

DOI 10.1021/acs.jcim.9b00105PMC PMC7371000

Published version of a preprint

Open this paper in CoChat — free account

Abstract

With renewed interest in free energy methods in contemporary structure-based drug design, there is a pressing need to validate against multiple targets and force fields to assess the overall ability of these methods to accurately predict relative binding free energies. We computed relative binding free energies using graphics processing unit accelerated thermodynamic integration (GPU-TI) on a data set originally assembled by Schrödinger, Inc. Using their GPU free energy code (FEP+) and the OPLS2.1 force field combined with the REST2 enhanced sampling approach, these authors obtained an overall MUE of 0.9 kcal/mol and an overall RMSD of 1.14 kcal/mol. In our study using GPU-TI from AMBER with the AMBER14SB/GAFF1.8 force field but without enhanced sampling, we obtained an overall MUE of 1.17 kcal/mol and an overall RMSD of 1.50 kcal/mol for the 330 perturbations contained in this data set. A more detailed analysis of our results suggested that the observed differences between the two studies arise from differences in sampling protocols along with differences in the force fields employed. Future work should address the problem of establishing benchmark quality results with robust statistical error bars obtained through multiple independent runs and enhanced sampling, which is possible with the GPU-accelerated features in AMBER.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1021/acs.jcim.9b00105

Cites

61 of 61 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

More rigorous free energy methods such as alchemical binding free energy calculations have become established for the computation of binding free energies of small molecules to proteins with MD.
“More rigorous free energy methods such as alchemical binding free energy calculations have become established for the computation of binding free energies of small molecules to proteins with MD.55−60”
From Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints (2025) · cites this paper as [59]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems
Darden, T., York, D., & Pedersen, L. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464397 · Cited by 29,588 · see citing works
Self-citation
A smooth particle mesh Ewald method
Essmann, U., Perera, L., Berkowitz, M., Darden, T., Lee, H.-C., & Pedersen, L. (1995) · The Journal of Chemical Physics · doi:10.1063/1.470117 · Cited by 21,329 · see citing works
Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes
Ryckaert, J., Ciccotti, G., & Berendsen, H. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90098-5 · Cited by 19,525 · see citing works
Development and testing of a general amber force field
Wang, J.-M., Wolf, R., Caldwell, J., Kollman, P., & Case, D. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20035 · Cited by 18,318 · see citing works
The missing term in effective pair potentials
Berendsen, H. J. C., Grigera, J. R., & Straatsma, T. P. (1987) · The Journal of Physical Chemistry · doi:10.1021/j100308a038 · Cited by 13,313 · see citing works
CHARMM General Force Field (CGenFF): A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields
Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., Darian, E., Guvench, O., Lopes, P. E. M., Vorobyov, I., et al. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21367 · Cited by 6,986 · see citing works
THE weighted histogram analysis method for free‐energy calculations on biomolecules. I. The method
Kumar, S., Rosenberg, J. M., Bouzida, D., Swendsen, R. H., & Kollman, P. A. (1992) · Journal of Computational Chemistry · doi:10.1002/jcc.540130812 · Cited by 6,808 · see citing works
Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling
Torrie, G. M., & Valleau, J. P. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90121-8 · Cited by 6,091 · see citing works
Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models
Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W., et al. (2000) · Accounts of Chemical Research · doi:10.1021/ar000033j · Cited by 4,978 · see citing works
Self-citation
Statistical Mechanics of Fluid Mixtures
Kirkwood, J. G. (1935) · The Journal of Chemical Physics · doi:10.1063/1.1749657 · Cited by 3,454 · see citing works
Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular Simulations
Joung, I., & Cheatham, T. (2008) · Journal of Physical Chemistry B · doi:10.1021/jp8001614 · Cited by 3,326 · see citing works
High‐Temperature Equation of State by a Perturbation Method. I. Nonpolar Gases
Zwanzig, R. (1954) · The Journal of Chemical Physics · doi:10.1063/1.1740409 · Cited by 2,923 · 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
Efficient estimation of free energy differences from Monte Carlo data
Bennett, C. H. (1976) · Journal of Computational Physics · doi:10.1016/0021-9991(76)90078-4 · Cited by 2,631 · see citing works
Free energy calculations: Applications to chemical and biochemical phenomena
Kollman, P. A. (1993) · Chemical Reviews · doi:10.1021/cr00023a004 · Cited by 2,131 · see citing works
Statistically optimal analysis of samples from multiple equilibrium states.
Shirts, M., & Chodera, J. (2008) · Journal of Chemical Physics · doi:10.1063/1.2978177 · Cited by 1,825 · see citing works
Current Status of the AMOEBA Polarizable Force Field
Ponder, J. W., Wu, C., Ren, P., Pande, V. S., Chodera, J. D., Schnieders, M. J., Haque, I., Mobley, D. L., Lambrecht, D. S., DiStasio, R. A., et al. (2010) · The Journal of Physical Chemistry B · doi:10.1021/jp910674d · Cited by 1,440 · 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
Multidimensional free‐energy calculations using the weighted histogram analysis method
Kumar, S., Rosenberg, J. M., Bouzida, D., Swendsen, R. H., & Kollman, P. A. (1995) · Journal of Computational Chemistry · doi:10.1002/jcc.540161104 · Cited by 868 · see citing works
Fast and accurate predictions of binding free energies using MM‐PBSA and MM‐GBSA
Rastelli, G., Río, A. D., Degliesposti, G., & Sgobba, M. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21372 · Cited by 838 · see citing works
Extension to the weighted histogram analysis method: combining umbrella sampling with free energy calculations
Souaille, M., & Roux, B. (2001) · Computer Physics Communications · doi:10.1016/s0010-4655(00)00215-0 · Cited by 779 · see citing works
Free energy calculations: a breakthrough for modeling organic chemistry in solution
Jorgensen, W. L. (1989) · Accounts of Chemical Research · doi:10.1021/ar00161a004 · Cited by 637 · see citing works
Statistical Mechanical Theory of Transport Processes. VII. The Coefficient of Thermal Conductivity of Monatomic Liquids
Zwanzig, R. W., Kirkwood, J. G., Oppenheim, I., & Alder, B. J. (1954) · The Journal of Chemical Physics · doi:10.1063/1.1740193 · Cited by 553 · see citing works
Binding of a Diverse Set of Ligands to Avidin and Streptavidin: An Accurate Quantitative Prediction of Their Relative Affinities by a Combination of Molecular Mechanics and Continuum Solvent Models
Kuhn, B., & Kollman, P. A. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm000241h · Cited by 506 · see citing works
GPU-Accelerated Molecular Dynamics and Free Energy Methods in Amber18: Performance Enhancements and New Features
Lee, T., Cerutti, D. S., Mermelstein, D. J., Lin, C. H., LeGrand, S., Giese, T. J., Roitberg, A. E., Case, D. A., Walker, R. C., & York, D. M. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00462 · Cited by 487 · see citing works
Self-citation
Predicting Binding Free Energies: Frontiers and Benchmarks
Mobley, D. L., & Gilson, M. K. (2017) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-070816-033654 · Cited by 396 · see citing works
Ligand Binding Affinities from MD Simulations
Åqvist, J., Luzhkov, V. B., & Brandsdal, B. O. (2002) · Accounts of Chemical Research · doi:10.1021/ar010014p · Cited by 380 · see citing works
λ‐dynamics: A new approach to free energy calculations
Kong, X., & Brooks, C. L. (1996) · The Journal of Chemical Physics · doi:10.1063/1.472109 · Cited by 345 · see citing works
Nonlinear scaling schemes for Lennard-Jones interactions in free energy calculations.
Steinbrecher, T., Mobley, D., & Case, D. (2007) · Journal of Chemical Physics · doi:10.1063/1.2799191 · Cited by 310 · see citing works
Calculations of antibody-antigen interactions: microscopic and semi-microscopic evaluation of the free energies of binding of phosphorylcholine analogs to McPC603
Lee, F. S., Chu, Z. T., Bolger, M. B., & Warshel, A. (1992) · Protein Engineering Design and Selection · doi:10.1093/protein/5.3.215 · Cited by 303 · see citing works
Free Energy Perturbation Hamiltonian Replica-Exchange Molecular Dynamics (FEP/H-REMD) for Absolute Ligand Binding Free Energy Calculations
Jiang, W., & Roux, B. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct1001768 · Cited by 241 · see citing works
Soft‐core potentials in thermodynamic integration: Comparing one‐ and two‐step transformations
Steinbrecher, T. B., Joung, I., & Case, D. A. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21909 · Cited by 234 · see citing works
Perspective: Alchemical free energy calculations for drug discovery.
Mobley, D., & Klimovich, P. V. (2012) · Journal of Chemical Physics · doi:10.1063/1.4769292 · Cited by 232 · see citing works
On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities
Wang, L., Berne, B. J., & Friesner, R. A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114017109 · Cited by 224 · see citing works
Modeling Local Structural Rearrangements Using FEP/REST: Application to Relative Binding Affinity Predictions of CDK2 Inhibitors
Wang, L., Deng, Y., Knight, J. L., Wu, Y., Kim, B., Sherman, W., Shelley, J. C., Lin, T., & Abel, R. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct300911a · Cited by 195 · see citing works
Free energy perturbation simulations of the inhibition of thermolysin: prediction of the free energy of binding of a new inhibitor
Merz, K. M., & Kollman, P. A. (1989) · Journal of the American Chemical Society · doi:10.1021/ja00197a022 · Cited by 139 · see citing works
Self-citation
Accurate Binding Free Energy Predictions in Fragment Optimization
Steinbrecher, T., Dahlgren, M. K., Cappel, D., Lin, T., Wang, L.-L., Krilov, G., Abel, R., Friesner, R., & Sherman, W. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00538 · Cited by 136 · see citing works
Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration
Lee, T., Hu, Y., Sherborne, B. S., Guo, Z., & York, D. M. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00102 · Cited by 132 · see citing works
Self-citation
Prospective Evaluation of Free Energy Calculations for the Prioritization of Cathepsin L Inhibitors
Kuhn, B., Tichý, M., Wang, L., Robinson, S. D., Martin, R. E., Kuglstatter, A., Benz, J., Giroud, M., Schirmeister, T., Abel, R., et al. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b01881 · Cited by 131 · see citing works
Application of Free Energy Perturbation for the Design of BACE1 Inhibitors
Ciordia, M., Pérez‐Benito, L., Delgado, F., Trabanco, A. A., & Tresadern, G. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00220 · Cited by 105 · see citing works
Improving the Efficiency of Free Energy Calculations in the Amber Molecular Dynamics Package
Kaus, J. W., Pierce, L., Walker, R. C., & McCammon, J. A. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400340s · Cited by 103 · see citing works
Linear Interaction Energy: Method and Applications in Drug Design
Gutiérrez‐de‐Terán, H., & Åqvist, J. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-465-0_20 · Cited by 100 · see citing works
Structure-Guided Design of AMP Mimics That Inhibit Fructose-1,6-bisphosphatase with High Affinity and Specificity
Erion, M. D., Dang, Q., Reddy, M. R., Kasibhatla, S. R., Huang, J., Lipscomb, W. N., & van Poelje, P. D. (2007) · Journal of the American Chemical Society · doi:10.1021/ja074869u · Cited by 92 · see citing works
A New Maximum Likelihood Approach for Free Energy Profile Construction from Molecular Simulations
Lee, T., Radak, B. K., Pabis, A., & York, D. M. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300703z · Cited by 91 · see citing works
Self-citation
A Multistep Approach to Structure-Based Drug Design: Studying Ligand Binding at the Human Neutrophil Elastase
Steinbrecher, T. B., Case, D. A., & Labahn, A. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0505720 · Cited by 84 · see citing works
Effects of Water Placement on Predictions of Binding Affinities for p38α MAP Kinase Inhibitors
Luccarelli, J., Michel, J., Tirado‐Rives, J., & Jorgensen, W. L. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct100504h · Cited by 83 · see citing works
Reproducibility of Free Energy Calculations across Different Molecular Simulation Software Packages.
Loeffler, H. H., Bosisio, S., Matos, G. D. R., Suh, D., Roux, B., Mobley, D., & Michel, J. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00544 · Cited by 81 · see citing works
Calculation of Relative Binding Free Energy Differences for Fructose 1,6-Bisphosphatase Inhibitors Using the Thermodynamic Cycle Perturbation Approach
Reddy, M. R., & Erion, M. D. (2001) · Journal of the American Chemical Society · doi:10.1021/ja0103288 · Cited by 80 · see citing works
Roadmaps through Free Energy Landscapes Calculated Using the Multidimensional vFEP Approach
Lee, T., Radak, B. K., Huang, M. S., Wong, K. Y., & York, D. M. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400691f · Cited by 71 · see citing works
Self-citation
Bornyl (3,4,5-trihydroxy)-cinnamate - An optimized human neutrophil elastase inhibitor designed by free energy calculations
Steinbrecher, T. B., Hrenn, A., Dormann, K. L., Merfort, I., & Labahn, A. (2007) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2007.11.070 · Cited by 67 · see citing works
Relative Binding Free Energy Calculations Applied to Protein Homology Models
Cappel, D., Hall, M., Lenselink, E. B., Beuming, T., Qi, J., Bradner, J., & Sherman, W. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00362 · Cited by 64 · see citing works
Absolute FKBP binding affinities obtained via nonequilibrium unbinding simulations
Ytreberg, F. M. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3119261 · Cited by 63 · see citing works
Overcoming dissipation in the calculation of standard binding free energies by ligand extraction
Velez‐Vega, C., & Gilson, M. K. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23398 · Cited by 59 · see citing works
Predicting Binding Free Energies of PDE2 Inhibitors. The Difficulties of Protein Conformation
Pérez‐Benito, L., Keränen, H., van Vlijmen, H., & Tresadern, G. (2018) · Scientific Reports · doi:10.1038/s41598-018-23039-5 · Cited by 57 · see citing works
Impact of calcium on N1 influenza neuraminidase dynamics and binding free energy
Lawrenz, M., Wereszczynski, J., Amaro, R. E., Walker, R. C., Roitberg, A. E., & McCammon, J. A. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22761 · Cited by 54 · see citing works
Computation of the binding affinities of catechol‐O‐methyltransferase inhibitors: Multisubstate relative free energy calculations
Palma, P. N., Bonifácio, M. J., Loureiro, A. I., & Soares-da-Silva, P. (2012) · Journal of Computational Chemistry · doi:10.1002/jcc.22926 · Cited by 52 · see citing works
Improved Binding Free Energy Predictions from Single-Reference Thermodynamic Integration Augmented with Hamiltonian Replica Exchange
Khavrutskii, I., & Wallqvist, A. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct2003786 · Cited by 41 · see citing works
Test MM-PB/SA on True Conformational Ensembles of Protein−Ligand Complexes
Li, Y., Liu, Z., & Wang, R. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100036a · Cited by 36 · see citing works
Homology modeling, force field design, and free energy simulation studies to optimize the activities of histone deacetylase inhibitors
Park, H., & Lee, S. (2004) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-004-2283-3 · Cited by 36 · see citing works
Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations
Raman, E. P., Vanommeslaeghe, K., & MacKerell, A. D. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300088r · Cited by 29 · see citing works
Relative Binding Free Energies of Adenine and Guanine to Damaged and Undamaged DNA in Human DNA Polymerase η: Clues for Fidelity and Overall Efficiency
Ucisik, M. N., & Hammes‐Schiffer, S. (2015) · Journal of the American Chemical Society · doi:10.1021/jacs.5b08451 · Cited by 13 · see citing works

Flagged first, then by citations

1 of the 62 references couldn’t be matched to a known paper.

Sorted by citations

Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery
Lee, T., Allen, B. K., Giese, T. J., Guo, Z., Li, P., Lin, C. H., McGee, T. D., Pearlman, D. A., Radak, B. K., Tao, Y., et al. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00613 · Cited by 379 · see citing works
Self-citation
Large scale relative protein ligand binding affinities using non-equilibrium alchemy
Gapsys, V., Pérez‐Benito, L., Aldeghi, M., Seeliger, D., van Vlijmen, H., Tresadern, G., & de Groot, B. L. (2019) · Chemical Science · doi:10.1039/c9sc03754c · Cited by 255 · see citing works
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 · see citing works
Best Practices for Alchemical Free Energy Calculations [Article v1.0].
Mey, A. S. J. S., Allen, B. K., Macdonald, H. E. B., Chodera, J. D., Hahn, D. F., Kuhn, M., Michel, J., Mobley, D. L., Naden, L. N., Prasad, S., et al. (2020) · eScholarship (California Digital Library) · Cited by 217 · see citing works
The maximal and current accuracy of rigorous protein-ligand binding free energy calculations
Ross, G. A., Lu, C., Scarabelli, G., Albanese, S. K., Houang, E., Abel, R., Harder, E. D., & Wang, L. (2023) · Communications Chemistry · doi:10.1038/s42004-023-01019-9 · Cited by 178 · see citing works
The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives
Niazi, S. K. (2023) · Drug Design Development and Therapy · doi:10.2147/dddt.s424991 · Cited by 146 · see citing works
Fast, Accurate, and Reliable Protocols for Routine Calculations of Protein–Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFF
He, X., Liu, S., Lee, T., Ji, B., Man, V. H., York, D. M., & Wang, J. (2020) · ACS Omega · doi:10.1021/acsomega.9b04233 · Cited by 123 · see citing works
Self-citation
Evolution of Alchemical Free Energy Methods in Drug Discovery
Song, L. F., & Merz, K. M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00547 · Cited by 115 · see citing works
Self-citation
Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo
Ross, G. A., Russell, E., Deng, Y., Lu, C. T., Harder, E. D., Abel, R., & Wang, L. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00660 · Cited by 103 · see citing works
Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentialsPreprint
Rufa, D. A., Macdonald, H. E. B., Fass, J., Wieder, M., Grinaway, P., Roitberg, A. E., Isayev, O., & Chodera, J. D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.29.227959 · Cited by 94 · see citing works
Chemical Space Exploration with Active Learning and Alchemical Free Energies
Khalak, Y., Tresadern, G., Hahn, D. F., de Groot, B. L., & Gapsys, V. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00752 · Cited by 90 · see citing works
Best Practices for Constructing, Preparing, and Evaluating Protein-Ligand Binding Affinity Benchmarks [Article v1.0]
Hahn, D. F., Bayly, C. I., Boby, M. L., Macdonald, H. E. B., Chodera, J. D., Gapsys, V., Mey, A. S. J. S., Mobley, D. L., Pérez‐Benito, L., Schindler, C., et al. (2022) · Living Journal of Computational Molecular Science · doi:10.33011/livecoms.4.1.1497 · Cited by 89 · see citing works
End-to-end differentiable construction of molecular mechanics force fields
Wang, Y., Fass, J., Kaminow, B., Herr, J. E., Rufa, D. A., Zhang, I., Pulido, I., Henry, M. M., Macdonald, H. E. B., Takaba, K., et al. (2022) · Chemical Science · doi:10.1039/d2sc02739a · Cited by 88 · see citing works
Structure and dynamics in drug discovery
Wei, H., & McCammon, J. A. (2024) · npj Drug Discovery. · doi:10.1038/s44386-024-00001-2 · Cited by 94 · see citing works
Metal-ion-chelating phenylalanine nanostructures reverse immune dysfunction and sensitize breast tumour to immune checkpoint blockade
Tan, M., Cao, G., Wang, R., Cheng, L., Huang, W., Yin, Y., Ma, H., Ho, S., Wang, Z., Zhu, M., et al. (2024) · Nature Nanotechnology · doi:10.1038/s41565-024-01758-3 · Cited by 83 · see citing works
Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization
Ustach, V. D., Lakkaraju, S. K., Jo, S., Yu, W., Jiang, W., & MacKerell, A. D. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00210 · Cited by 81 · see citing works
Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation
Deflorian, F., Pérez‐Benito, L., Lenselink, E. B., Congreve, M., van Vlijmen, H., Mason, J. S., de Graaf, C., & Tresadern, G. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00449 · Cited by 79 · see citing works
Identification of potential anti-TMPRSS2 natural products through homology modelling, virtual screening and molecular dynamics simulation studies
Chikhale, R. V., Gupta, V. K., Eldesoky, G. E., Wabaidur, S. M., Patil, S. A., & Islam, M. A. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1798813 · Cited by 77 · see citing works
Automated, Accurate, and Scalable Relative Protein–Ligand Binding Free-Energy Calculations Using Lambda Dynamics
Raman, E. P., Paul, T. J., Hayes, R. L., & Brooks, C. L. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00830 · Cited by 75 · see citing works
Promising inhibitors of main protease of novel corona virus to prevent the spread of COVID-19 using docking and molecular dynamics simulation
Kumar, D., Kumari, K., Vishvakarma, V. K., Jayaraj, A., Kumar, D., Venkatesh Kumar, R., Patel, R., Kumar, V., Dass, S. K., Chandra, R., et al. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1779131 · Cited by 73 · see citing works
Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling
Gusev, F., Gutkin, E. M., Kurnikova, M. G., & Isayev, O. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01052 · Cited by 73 · see citing works
Free Energy Methods in Drug Discovery—Introduction
Cournia, Z., Chipot, C. J., Roux, B., York, D. M., & Sherman, W. (2021) · ACS symposium series · doi:10.1021/bk-2021-1397.ch001 · Cited by 74 · see citing works
Self-citation
Improved Alchemical Free Energy Calculations with Optimized Smoothstep Softcore Potentials
Lee, T., Lin, Z., Allen, B. K., Lin, C. H., Radak, B. K., Tao, Y., Tsai, H., Sherman, W., & York, D. M. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00237 · Cited by 71 · see citing works
Self-citation
From computer-aided drug discovery to computer-driven drug discovery
Frye, L. L., Bhat, S., Akinsanya, K., & Abel, R. (2021) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2021.08.001 · Cited by 70 · see citing works
Multi-enzyme cascade for improving β-hydroxy-α-amino acids production by engineering L-threonine transaldolase and combining acetaldehyde elimination system
Xu, L., Wang, L., Su, B., Xu, X., & Lin, J. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.123439 · Cited by 64 · see citing works
Optimizing active learning for free energy calculations
Thompson, J., Walters, W. P., Feng, J. A., Pabon, N. A., Xu, H., Maser, M., Goldman, B. B., Moustakas, D. T., Schmidt, M., & York, F. (2022) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100050 · Cited by 59 · see citing works
Current State of Open Source Force Fields in Protein–Ligand Binding Affinity Predictions
Hahn, D. F., Gapsys, V., de Groot, B. L., Mobley, D. L., & Tresadern, G. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00417 · Cited by 58 · see citing works
Rapid and accurate estimation of protein–ligand relative binding affinities using site-identification by ligand competitive saturation
Goel, H., Hazel, A. J., Ustach, V. D., Jo, S., Yu, W., & MacKerell, A. D. (2021) · Chemical Science · doi:10.1039/d1sc01781k · Cited by 57 · see citing works
CHARMM-GUI Free Energy Calculator for Practical Ligand Binding Free Energy Simulations with AMBER
Zhang, H., Kim, S., Giese, T. J., Lee, T.-S., Lee, J.-M., York, D., & Im, W. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00747 · Cited by 52 · see citing works
Self-citation
Development of a Novel Multi-Epitope Vaccine Against Crimean-Congo Hemorrhagic Fever Virus: An Integrated Reverse Vaccinology, Vaccine Informatics and Biophysics Approach
Qamar, M. T. U., Ismail, S., Ahmad, S., Mirza, M. U., Abbasi, S. W., Ashfaq, U. A., & Chen, L. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.669812 · Cited by 54 · see citing works
Mechanistic origin of partial agonism of tetrahydrocannabinol for cannabinoid receptors
Dutta, S., Selvam, B., Das, A., & Shukla, D. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.101764 · Cited by 42 · see citing works
Pre-Exascale Computing of Protein–Ligand Binding Free Energies with Open Source Software for Drug Design
Gapsys, V., Hahn, D. F., Tresadern, G., Mobley, D. L., Rampp, M., & de Groot, B. L. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01445 · Cited by 51 · see citing works
Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies
Scheen, J., Wu, W., Mey, A. S. J. S., Tosco, P., Mackey, M. D., & Michel, J. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00600 · Cited by 48 · see citing works
A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Predictions: New Opportunities and Challenges for Drug Discovery
Lin, Z., Zou, J., Liu, S., Peng, C., Li, Z., Wan, X., Dong, F., Yin, J., Gobbo, G., Chen, Y., et al. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01329 · Cited by 50 · see citing works
Prioritizing Small Sets of Molecules for Synthesis through in‐silico Tools: A Comparison of Common Ranking Methods
Breznik, M., Ge, Y., Bluck, J. P., Briem, H., Hahn, D. F., Christ, C. D., Mortier, J., Mobley, D. L., & Meier, K. (2022) · ChemMedChem · doi:10.1002/cmdc.202200425 · Cited by 44 · see citing works
Novel Directions in Free Energy Methods and Applications
Armacost, K. A., Riniker, S., & Cournia, Z. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01174 · Cited by 45 · see citing works
In Silico Study of Coumarins and Quinolines Derivatives as Potent Inhibitors of SARS-CoV-2 Main Protease
Yáñez, O., Osorio, M. I., Uriarte, E., Areche, C., Tiznado, W., Perez-Donoso, J. M., García‐Beltrán, O., & González-Nilo, F. D. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2020.595097 · 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
Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery
Qian, R., Xue, J., Xu, Y. C., & Huang, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01024 · Cited by 41 · see citing works
Accounting for the Central Role of Interfacial Water in Protein–Ligand Binding Free Energy Calculations
Ben‐Shalom, I., Lin, Z., Radak, B. K., Lin, C. H., Sherman, W., & Gilson, M. K. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00785 · Cited by 39 · see citing works
An In Silico Investigation to Explore Anti-Cancer Potential of Foeniculum vulgare Mill. Phytoconstituents for the Management of Human Breast Cancer
Kaur, B., Rolta, R., Salaria, D., Kumar, B., Fadare, O. A., da Costa, R. A., Ahmad, A., Al-Rawi, M. B. A., Raish, M., & Rather, I. A. (2022) · Molecules · doi:10.3390/molecules27134077 · Cited by 39 · see citing works
Phytoconstituents of traditional Himalayan Herbs as potential inhibitors of Human Papillomavirus (HPV-18) for cervical cancer treatment: An In silico Approach
Salaria, D., Rolta, R., Mehta, J., Awofisayo, O., Fadare, O. A., Kaur, B., Kumar, B., da Costa, R. A., Chandel, S. R., Kaushik, N., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265420 · Cited by 39 · see citing works
AMBER Drug Discovery Boost Tools: Automated Workflow for Production Free-Energy Simulation Setup and Analysis (ProFESSA)
Ganguly, A., Tsai, H., Fernández-Pendás, M., Lee, T., Giese, T. J., & York, D. M. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00879 · Cited by 38 · see citing works
Self-citation
Coumarin-Chalcone Hybrids as Inhibitors of MAO-B: Biological Activity and In Silico Studies
Moya‐Alvarado, G., Yáñez, O., Morales, N., González‐González, A., Areche, C., Núñez, M. T., Fierro, A., & García‐Beltrán, O. (2021) · Molecules · doi:10.3390/molecules26092430 · Cited by 37 · see citing works
Investigating Potential Inhibitory Effect of Uncaria tomentosa (Cat’s Claw) against the Main Protease 3CL pro of SARS‐CoV‐2 by Molecular Modeling
Yepes, A. F., Herrera-Calderón, O., Sánchez‐Aparicio, J., Tiessler‐Sala, L., Maréchal, J., & Cardona‐Galeano, W. (2020) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2020/4932572 · Cited by 36 · see citing works
Methodological uncertainties in drug-receptor binding free energy predictions based on classical molecular dynamics
Procacci, P. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.08.001 · Cited by 36 · see citing works
Predicting the affinity of halogenated reversible covalent inhibitors through relative binding free energy
Lameira, J., Bonatto, V., Cianni, L., Rocho, F. D. R., Leitão, A., & Montanari, C. A. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp04820k · Cited by 35 · see citing works
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules
Gelžinytė, E., Öeren, M., Segall, M., & Cśanyi, G. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00710 · Cited by 33 · see citing works
2-Oxaadamant-1-yl Ureas as Soluble Epoxide Hydrolase Inhibitors: In Vivo Evaluation in a Murine Model of Acute Pancreatitis
Codony, S., Pujol, E., Pizarro‐Delgado, J., Feixas, F., Valverde, E., Loza, M. I., Brea, J. M., Sáez, E., Oyarzábal, J., Pineda‐Lucena, A., et al. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c00310 · Cited by 33 · see citing works
Automated relative binding free energy calculations from SMILES to ΔΔG
Moore, J. H., Margreitter, C., Janet, J. P., Engkvist, O., de Groot, B. L., & Gapsys, V. (2023) · Communications Chemistry · doi:10.1038/s42004-023-00859-9 · Cited by 32 · see citing works
Binding Thermodynamics of Host–Guest Systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative
Slochower, D. R., Henriksen, N. M., Wang, L., Chodera, J. D., Mobley, D. L., & Gilson, M. K. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00748 · Cited by 32 · see citing works
Heterocyclic Substitutions Greatly Improve Affinity and Stability of Folic Acid towards FRα. an In Silico Insight
Al-Thiabat, M. G., Saqallah, F. G., Gazzali, A. M., Mohtar, N., Yap, B. K., Choong, Y. S., & Wahab, H. A. (2021) · Molecules · doi:10.3390/molecules26041079 · Cited by 32 · see citing works
Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein–ligand binding affinities
Yasuda, I., Endo, K., Yamamoto, E., Hirano, Y., & Yasuoka, K. (2022) · Communications Biology · doi:10.1038/s42003-022-03416-7 · Cited by 34 · see citing works
NHA1 is a cation/proton antiporter essential for the water-conserving functions of the rectal complex in Tribolium castaneum
Naseem, M. T., Beaven, R., Koyama, T., Naz, S., Su, S.-Y., Leader, D. P., Klaerke, D. A., Calløe, K., Denholm, B., & Halberg, K. A. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2217084120 · Cited by 32 · see citing works
Promiscuous recognition of MR1 drives self-reactive mucosal-associated invariant T cell responses
Chancellor, A., Simmons, R. A., Khanolkar, R. C., Nosi, V., Beshirova, A., Berloffa, G., Colombo, R., Karuppiah, V., Pentier, J. M., Tubb, V., et al. (2023) · The Journal of Experimental Medicine · doi:10.1084/jem.20221939 · Cited by 30 · see citing works
AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking
Pan, X., Wang, H., Zhang, Y., Wang, X., Li, C., Ji, C., & Zhang, J. Z. H. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01537 · Cited by 29 · see citing works
Discovery of Novel Inhibitors of BRD4 for Treating Prostate Cancer: A Comprehensive Case Study for Considering Water Networks in Virtual Screening and Drug Design
Zhong, H., Wang, X., Chen, S., Wang, Z., Wang, H., Xu, L., Hou, T., Yao, X., Li, D., & Pan, P. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c00996 · Cited by 29 · see citing works
Redox sensitive human mitochondrial aconitase and its interaction with frataxin: In vitro and in silico studies confirm that it takes two to tango
Mansilla, S., Tórtora, V., Pignataro, M. F., Sastre, S., Castro, I. H., Chiribao, M. L., Robello, C., Zeida, A., Santos, J., & Castro, L. (2023) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2023.01.028 · Cited by 27 · see citing works
Validation of Free Energy Methods in AMBER
Tsai, H., Tao, Y., Lee, T., Merz, K. M., & York, D. M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00285 · Cited by 28 · see citing works
Self-citation
Theobroma cacao L. compounds: Theoretical study and molecular modeling as inhibitors of main SARS-CoV-2 protease
Yáñez, O., Osorio, M. I., Areche, C., Vásquez‐Espinal, A., Bravo, J., Sandoval‐Aldana, A., Perez-Donoso, J. M., González-Nilo, F. D., Matos, M. J., Osorio, E. H., et al. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2021.111764 · Cited by 28 · see citing works
Prediction of ligand binding mode among multiple cross-docking poses by molecular dynamics simulations
Liu, K., & Kokubo, H. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00340-y · Cited by 27 · see citing works
Practical Guidance for Consensus Scoring and Force Field Selection in Protein-Ligand Binding Free Energy Simulations
Zhang, H., Kim, S., & Im, W. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01115 · Cited by 27 · see citing works
Synthesis, characterization and in-silico assessment of novel thiazolidinone derivatives for cyclin-dependent kinases-2 inhibitors
Abdullah, J. A., Aldahham, B. J. M., Rabeea, M. A., Asmary, F. A., Alhajri, H. M., & Islam, M. A. (2020) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2020.129311 · Cited by 27 · see citing works
Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method
Sun, Y., He, X., Hou, T., Cai, L., Man, V. H., & Wang, J. (2023) · Journal of Computational Chemistry · doi:10.1002/jcc.27089 · Cited by 26 · see citing works
The prediction of protein–ligand unbinding for modern drug discovery
Zhang, Q., Zhao, N., Meng, X., Yu, F., Yao, X., & Liu, H. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2002298 · Cited by 26 · see citing works
Application of site-identification by ligand competitive saturation in computer-aided drug design
Goel, H., Hazel, A. J., Yu, W., Jo, S., & MacKerell, A. D. (2021) · New Journal of Chemistry · doi:10.1039/d1nj04028f · Cited by 25 · see citing works
TLK1-mediated RAD54 phosphorylation spatio-temporally regulates Homologous Recombination Repair
Ghosh, I., Kwon, Y., Shabestari, A. B., Chikhale, R. V., Chen, J., Wiese, C., Sung, P., & Benedetti, A. D. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad589 · Cited by 26 · see citing works
A Computational Approach Applied to the Study of Potential Allosteric Inhibitors Protease NS2B/NS3 from Dengue Virus
da Costa, R. A., da Rocha, J. A. P., Pinheiro, A. S., da Costa, A. D. S. S., da Rocha, E. C. M., Silva, R. C., Gonçalves, A. D. S., Santos, C. B. R., & Brasil, D. D. S. B. (2022) · Molecules · doi:10.3390/molecules27134118 · Cited by 25 · see citing works
Improving the thermostability of alginate lyase FlAlyA with high expression by computer-aided rational design for industrial preparation of alginate oligosaccharides
Zhang, X., Li, W., Pan, L., Yang, L., Li, H., Ji, F., Zhang, Y., Tang, H., & Yang, D. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.1011273 · Cited by 25 · see citing works
Pharmacoinformatics-based identification of transmembrane protease serine-2 inhibitors from Morus Alba as SARS-CoV-2 cell entry inhibitors
Shakya, A., Chikhale, R. V., Bhat, H. R., Alasmary, F. A., Almutairi, T. M., Ghosh, S. K., Alhajri, H. M., Alissa, S. A., Nagar, S., & Islam, M. A. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10209-3 · Cited by 24 · see citing works
Effects of astragaloside IV on glucocorticoid‐induced avascular necrosis of the femoral head via regulating Akt‐related pathways
Shan, H., Lin, Y., Yin, F., Pan, C., Hou, J., Wu, T., Xia, W., Zuo, R., Cao, B., Jiang, C., et al. (2023) · Cell Proliferation · doi:10.1111/cpr.13485 · Cited by 24 · see citing works
Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set
Croitoru, A., Kumar, A., Lambry, J., Lee, J., Sharif, S., Yu, W.-B., MacKerell, A., & Aleksandrov, A. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00046 · Cited by 21 · see citing works
Designing of a chimeric multiepitope vaccine against bancroftian lymphatic filariasis through immunoinformatics approaches
Parvin, R., Masum, M. H. U., Ferdous, J., Mahdeen, A. A., & Khan, M. S. I. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310398 · Cited by 22 · see citing works
Dual A1/A3 Adenosine Receptor Antagonists: Binding Kinetics and Structure−Activity Relationship Studies Using Mutagenesis and Alchemical Binding Free Energy Calculations
Stampelou, M., Suchankova, A., Tzortzini, E., Dhingra, L., Barkan, K., Lougiakis, N., Marakos, P., Pouli, N., Ladds, G., & Kolocouris, A. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c01123 · Cited by 20 · see citing works
Identification of potential inhibitors of Zika virus NS5 RNA-dependent RNA polymerase through virtual screening and molecular dynamic simulations
Noreen, Ali, R., Badshah, S. L., Faheem, M. Q., Abbasi, S. W., Ullah, R., Bari, A., Jamal, S. B., Mahmood, H. M., Haider, A., et al. (2020) · Saudi Pharmaceutical Journal · doi:10.1016/j.jsps.2020.10.005 · Cited by 20 · see citing works
Robust, Efficient and Automated Methods for Accurate Prediction of Protein-Ligand Binding Affinities in AMBER Drug Discovery Boost
Lee, T., Tsai, H., Ganguly, A., Giese, T. J., & York, D. M. (2021) · ACS symposium series · doi:10.1021/bk-2021-1397.ch007 · Cited by 20 · see citing works
Self-citation
Expanding the boundaries of ligand–target modeling by exascale calculations
Bolnykh, V., Rossetti, G., Röthlisberger, U., & Carloni, P. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1535 · Cited by 19 · see citing works
Expanded Ensemble Methods Can be Used to Accurately Predict Protein-Ligand Relative Binding Free Energies
Zhang, S., Hahn, D. F., Shirts, M. R., & Voelz, V. A. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00513 · Cited by 19 · see citing works
Protein–Ligand Binding Free-Energy Calculations with ARROW─A Purely First-Principles Parameterized Polarizable Force Field
Nawrocki, G., Leontyev, I. V., Sakipov, S. N., Darkhovskiy, M., Kurnikov, I. V., Pereyaslavets, L. B., Kamath, G., Воронина, Е. Н., Butin, O., Illarionov, A., et al. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00930 · Cited by 18 · see citing works
Carvacrol: An In Silico Approach of a Candidate Drug on HER2, PI3Kα, mTOR, hER‐α, PR, and EGFR Receptors in the Breast Cancer
Herrera-Calderón, O., Yepes, A. F., Quintero‐Saumeth, J., Rojas-Armas, J. P., Palomino-Pacheco, M., Ortiz-Sánchez, J. M., Cieza-Macedo, E. C., Arroyo-Acevedo, J. L., Figueroa-Salvador, L., Peña-Rojas, G., et al. (2020) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2020/8830665 · Cited by 18 · see citing works
A polycyclic scaffold identified by structure-based drug design effectively inhibits the human P2X7 receptor
Oken, A. C., Turcu, A. L., Tzortzini, E., Georgiou, K., Nagel, J., Westermann, F. G., Barniol‐Xicota, M., Seidler, J., Kim, G., Lee, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62643-8 · Cited by 17 · see citing works
Docking, Binding Free Energy Calculations and In Vitro Characterization of Pyrazine Linked 2-Aminobenzamides as Novel Class I Histone Deacetylase (HDAC) Inhibitors
Bülbül, E. F., Melesina, J., Ibrahim, H. S., Abdelsalam, M. A. M., Vecchio, A., Robaa, D., Zessin, M., Schutkowski, M., & Sippl, W. (2022) · Molecules · doi:10.3390/molecules27082526 · Cited by 17 · see citing works
Multi-epitope-based vaccine models prioritization against Astrovirus MLB1 using immunoinformatics and reverse vaccinology approaches
Ali, A., Ali, S. L., Alamri, A., Khatrawi, E. M., Baiduissenova, A., Сулейменова, Ф. М., Mishra, V. K., Khan, A., Утеуович, Д. М., & Askarova, G. (2024) · Journal of Genetic Engineering and Biotechnology · doi:10.1016/j.jgeb.2024.100451 · Cited by 17 · see citing works
Promising inhibitors of nsp2 of CHIKV using molecular docking and temperature-dependent molecular dynamics simulations
Meena, M. K., Kumar, D., Kumari, K., Kaushik, N. K., Kumar, R. V., Bahadur, I., Vodwal, L., & Singh, P. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1873863 · Cited by 17 · see citing works
A Benchmark of Electrostatic Method Performance in Relative Binding Free Energy Calculations
Ge, Y.-H., Hahn, D. F., & Mobley, D. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01424 · Cited by 17 · see citing works
A rationally designed 2C inhibitor prevents enterovirus D68-infected mice from developing paralysis
Li, K., Rudy, M. J., Hu, Y., Tan, H., Lambrinidis, G., Wu, X., Georgiou, K., Tan, B., Frost, J., Wilson, C. J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61083-8 · Cited by 16 · see citing works
A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19
Ruchawapol, C., Fu, W., & Xu, H. (2022) · Phytomedicine · doi:10.1016/j.phymed.2022.154324 · Cited by 16 · see citing works
Modeling receptor flexibility in the structure-based design of KRASG12C inhibitors
Zhu, K., Li, C., Wu, K. Y., Mohr, C., Li, X., & Lanman, B. A. (2022) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-022-00467-0 · Cited by 16 · see citing works
Dealing with Induced Fit, Conformational Selection, and Secondary Poses in Molecular Dynamics Simulations for Reliable Free Energy Predictions
Procacci, P. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00867 · Cited by 16 · see citing works
Exploring the effect of temperature on inhibition of non-structural protease 3 of Chikungunya virus using molecular dynamics simulations and thermodynamics parameters
Kumar, R., Meena, M. K., Kumari, K., Kumar, R., Bahadur, I., Jain, P., & Singh, P. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2021.116164 · Cited by 16 · see citing works
Molecular docking and molecular dynamics study Lianhua Qingwen granules (LHQW) treats COVID-19 by inhibiting inflammatory response and regulating cell survival
Cao, J., Gong, Y., Wu, M., Xiong, L., Chen, S., Huang, H., Zhou, X., Ying-chun, P., Shen, X., Qu, J., et al. (2022) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2022.1044770 · Cited by 16 · see citing works
Development of a New Scoring Function for Virtual Screening: APBScore
Bao, J., He, X. D., & Zhang, J. Z. H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00474 · Cited by 15 · see citing works
Molecular recognition between bacterial phosphorothioate DNA and sulfur-binding domain (SBD): competition between the water cage and chalcogen-hydrophobic packet
Li, J., Wan, H., Zhang, H., Wang, X., Liu, G., Wu, G., He, X., Deng, Z., & Zhao, Y. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp00291d · Cited by 15 · see citing works
In vitro and in silico analysis of galanthine from Zephyranthes carinata as an inhibitor of acetylcholinesterase
Sierra, K., de Andrade, J. P., Tallini, L. R., Osorio, E. H., Yáñez, O., Osorio, M. I., Oleas, N. H., García‐Beltrán, O., Borges, W. D. S., Bastida, J., et al. (2022) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2022.113016 · Cited by 15 · see citing works
Investigation into the effect and mechanism of dapagliflozin against renal interstitial fibrosis based on transcriptome and network pharmacology
Liu, Y., Wang, Y., Chen, S., Bai, L., Xie, X., Zhang, L., & Wang, X. (2022) · International Immunopharmacology · doi:10.1016/j.intimp.2022.109195 · Cited by 16 · see citing works
Binding Thermodynamics and Dissociation Kinetics Analysis Uncover the Key Structural Motifs of Phenoxyphenol Derivatives as the Direct InhA Inhibitors and the Hotspot Residues of InhA
Zhang, Q., Han, J., Zhu, Y., Tan, S., & Liu, H. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231710102 · Cited by 14 · see citing works
Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A 3 Receptor
Stampelou, M., Ladds, G., & Kolocouris, A. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c05986 · Cited by 14 · see citing works
Efficient synthesis of new azo-sulfonamide derivatives and investigation of their molecular docking and cytotoxicity results
Mahmoodi, N. O., Ahmadi, A., Nyaki, H. Y., Nahzomi, H. T., & Kokhdan, E. P. (2022) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2022.104383 · Cited by 13 · see citing works
Weighted gene co-expression network analysis and single-cell sequence analysis uncover immune landscape and reveal hub genes of necroptosis in macrophages in myocardial ischaemia–reperfusion injury
Ma, X., Xie, J., Li, B., Shan, H., Jia, Z., Liu, W., Dong, Y., Han, S., & Jin, Q. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.112761 · Cited by 13 · see citing works
Assessing the effect of forcefield parameter sets on the accuracy of relative binding free energy calculations
Sun, S., & Huggins, D. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.972162 · Cited by 11 · see citing works

Validation of AMBER/GAFF for Relative Free Energy CalculationsPreprint
Song, L., Lee, T., Zhu, C., York, D. M., & Merz, K. M. (2019) · ChemRxiv · doi:10.26434/chemrxiv.7653434 · Cited by 4 · see citing works
Self-citation
Validation of Free Energy Methods in AMBER
Tsai, H., Tao, Y., Lee, T., Merz, K. M., & York, D. M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00285 · Cited by 28 · see citing works
Self-citation
CHARMM-GUI Free Energy Calculator for Practical Ligand Binding Free Energy Simulations with AMBER
Zhang, H., Kim, S., Giese, T. J., Lee, T.-S., Lee, J.-M., York, D., & Im, W. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00747 · Cited by 52 · see citing works
Self-citation
Fast, Accurate, and Reliable Protocols for Routine Calculations of Protein–Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFF
He, X., Liu, S., Lee, T., Ji, B., Man, V. H., York, D. M., & Wang, J. (2020) · ACS Omega · doi:10.1021/acsomega.9b04233 · Cited by 123 · see citing works
Self-citation
Accuracy Assessment and Automation of Free Energy Calculations for Drug Design
Christ, C. D., & Fox, T. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci4004199 · Cited by 120 · see citing works

Authors: Song, L. F., Lee, T.-S., Zhu, C., York, D. M., & Merz, K. M.
Affiliations: Michigan State University (US)
Rutgers, The State University of New Jersey (US)
Corresponding author: Merz, Kenneth M.
ORCID: Song, Lin Frank
Lee, Tai-Sung
Zhu, Chun
York, Darrin M.
Merz, Kenneth M.
Venue: Journal of Chemical Information and Modeling
Publisher: American Chemical Society (ACS)
Published: 2019-06-06
Volume: 59
Issue: 7
Pages: 3128-3135
ISSN: 1549-9596 (ISSN-L)
1549-960X
DOI: 10.1021/acs.jcim.9b00105
PMC: PMC7371000
OpenAlex: W2949467224
Cited by: 234
References: 62
Citations by year: 2026: 20 · 2025: 32 · 2024: 37 · 2023: 35 · 2022: 45 · 2021: 30 · 2020: 31 · 2019: 4
FWCI: 18.88
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-045 (stm-asf)
Locations: Journal of Chemical Information and Modeling (publishedVersion)
ChemRxiv (acceptedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Royal College of Surgeons in Ireland (submittedVersion, cc-by)
Funders: Efficacy and Mechanism Evaluation Programme (GM107485)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords: relative binding free energy, thermodynamic integration, GPU acceleration, structure-based drug design, benchmarking, enhanced sampling, REST2, free energy perturbation, ligand binding prediction
MeSH terms: Computer Graphics, Models, Molecular, Protein Binding, Software, Thermodynamics, Drug Design, Molecular Structure, Molecular Dynamics Simulation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Using AMBER18 for Relative Free Energy Calculations

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks