Publications

Highlights

(For a full list see below or go to Google Scholar)

Benchmarking active learning protocols for ligand binding affinity prediction

Benchmarking active learning trategies for best performance on labelling data for binding affinities

R. Gorantla, A. Kubincova, B. Suutari, B. P. Cossins, A. S.J.S. Mey

J. Chem. Inf. Model. (2024)

The paper was featured in Practical Cheminformatics

SILVR: Guided Diffusion for Molecule Generation

SILVR is a method for conditioning a pre-trained equivariant diffusion model to generate new molecules from X-ray fragements.

Nicholas T. Runcie, Antonia S.J.S. Mey

J. Chem. Inf. Model. 63, 19, 5996–6005 (2023)

The paper was selected as an Editor’s Choice article

Allosteric effects in a catalytically impaired variant of the enzyme Cyclophilin A may be explained by changes in nano-microsecond time scale motions

Molecular simulations can give an alternative explanation for the reduced catalytic rate of different Cyclophilin A mutants that is experimentally testable.

P. Wapeesittipan, A.S.J.S. Mey, M. Walkinshaw, J. Michel

Comms. Chem. 2 41 (2019)

Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations

Accounting for differently charged ligands can be difficult in simulations, a framework for charging corrections was tested as part of a blinded challenge.

A.S.J.S. Mey, J. Juárez-Jiménez, J. Michel

J. Comput. Aided. Mol. Des. 32, 199 (2018)

Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R Grand Challenge

Alchemical free energy calculation for HSP90-α using SOMD give competitive results in a blinded prediction challenge.

A.S.J.S. Mey, J. Juárez-Jiménez, A. Hennessy, J. Michel

Bioorg. Med. Chem. 24, 4890 (2016)

Dynamic Properties of Forcefields

Protein dynamics is highly dependent on the choice of molecular forcefield. The speed of different forcefields is assessed.

F. Vitalini, A.S.J.S. Mey, F. Noé and B.G. Keller

J. Chem. Phys. 142, 084101 (2015)

Most read article of in JCP in 2015

 

Preprints

  1. Accurate and Robust Analysis of Molecular Kinetics with Random Features
    2. H. Sprink, Y. Zhu, A.S.J.S. Mey, and F. Nüske
    ChemRxiv 2025-fz7rt
  2. A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data
    3. H. MacDermott-Opeskin et al.
    chemrxiv-2025-zd9mr
  3. mRNA interactions with disordered regions control protein activity
    4. Y. Luo, S. Pratihar, E. Horste, S. Mitschka, A. S.J.S. Mey, H.M. Al-Hashimi, C. Mayr
    bioRxiv 2023.02.18.529068

Full List

  1. Explainable Active Learning Framework for Ligand Binding Affinity Prediction
    2. S.P. Srivastava, R. Gorantla, S. Krishna Chundru, C.J.R. Winkelman, A.S.J.S. Mey and R. Kumar Singh
    Digital Discoveries (2025)
  2. Learning Binding Affinities via Fine-Tuning of Protein and Ligand Language Models
    3. R. Gorantla, A.P. Gema, I.X. Yang, A. Serrano-Morras, B. Suutari, J. Juarez-Jimenez, A.S.J.S. Mey
    J. Chem. Inf. Model., 65, 12279 (2025)
  3. IR spectroscopy: from experimental spectra to high-resolution structural analysis by integrating simulations and machine learning
    4. M. Scherlo, D. Phillips, R. Künne, E. Ippoliti, K. Gerwert, C. Kötting, P. Carloni, A.S.J.S. Mey, and T. Rudack
    J. Phys. Chem. B, 129, 11652 (2025)
  4. All Roads Lead to Carbinolamine: QM/MM Study of Enzymatic C-N Bond Cleavage in Anaerobic Glycyl Radical Enzyme Choline Trime-thylamine-Lyase (CutC)
    5. M. Hanzevacki, J.J. Güven, P. Hinchliffe, J. Shaw, A.S.J.S. Mey, N. Fey, J. Spencer, and A.J. Mulholland
    J. Phys. Chem. B, 129, 9322 (2025)
  5. Protocols for metallo- and serine-β-lactamase free energy predictions: insights from cross-class inhibitors
    6. J. J. Güven, M. Hanževački, P. Kalita, A. J.Mulholland, A. S.J.S. Mey
    J. Phys. Chem. B, 128, 12416 (2024)
  6. Comparison of Methodologies for Absolute Binding Free Energy Calculations of Ligands to Intrinsically Disordered Proteins
    7. M. Papadourakis, Z. Cournia, A. S.J.S. Mey, J. Michel
    J. Chem. Theory Comput., 20, 9699–9707 (2024)
  7. Digital skills in chemical education
    8. A. R. McCluskey, M. Rivera, A. S.J.S. Mey
    Nat. Chem. 16, 1383-1384 (2024)
  8. Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictions
    9. C. Nauck, R. Gorantla, M. Lindner, K. Schürholt, A. S.J.S. Mey, F. Hellmann
    ICML workshop paper
  9. Sire: An Interoperability Engine for Prototyping Algorithms and Exchanging Information Between Molecular Simulation Programs
    10. C. Woods, L. Hedges, A. J. Mulholland, M. Malaisree, P. Tosco, H. Loeffler, M. Suruzhon, M. Burman, S. Bariami, S. Bosisio, G. Calabro, F. Clark, A. S.J.S. Mey, J. Michel
    J. Chem. Phys. 160, 202503 (2024)
  10. Benchmarking active learning protocols for ligand binding affinity prediction
    11. R. Gorantla, A. Kubincova, B. Suutari, B. P. Cossins, A. S.J.S. Mey
    J. Chem. Inf. Model. (2024)
  11. Markov State Models: to optimize or not to optimize
    12. R. Arbon, Y. Zhu, A. S.J.S. Mey
    J. Chem. Theory Comput. 20, 977–988 (2024)
  12. A suite of tutorials for the BioSimSpace framework for interoperable biomolecular simulation
    13. L. O. Hedges, S. Bariami, M. Burman, F. Clark, B. P. Cossins, A. Hardie, A. M. Herz, D. Lukauskis, A. S.J.S. Mey, J. Michel, J. Scheen, M. Suruzhon, C. J. Woods, Z. Wu
    Living Journal of Computational Molecular Science 5 (1), 2375-2375 (2023)
  13. From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction
    14. Rohan Gorantla, Alzbeta Kubincova, Andrea Y. Weisse, Antonia S.J.S. Mey
    J. Chem. Inf. Model. (2023)
  14. SILVR: Guided Diffusion for Molecule Generation
    15. Nicholas T. Runcie, Antonia S.J.S. Mey
    J. Chem. Inf. Model. 63, 19, 5996–6005 (2023)
  15. Course Materials for an Introduction to Data-Driven Chemistry
    16. James Cumby, Valentina Erastova, Matteo T. Degiacomi, J. Jasmin Güven, Claire L. Hobday, Antonia S.J.S. Mey, Hannah Pollak, Rafal Szabla
    J. Open Source Ed. 6, 192 (2023)
  16. What geometrically constrained models can tell us about real-world protein contact maps
    17. J. Jasmin Güven, Nora Molkenthin, Steffen Mühle, Antonia S.J.S. Mey
    Phys. Biol. 20 046004 (2023)
  17. Efficient Purification of Cowpea Chlorotic Mottle Virus by a Novel Peptide Aptamer
    18. G. Tscheuschner, M. Ponader, C. Raab, P. S. Weider, R. Hartfiel, J. O. Kaufmann, J. L. Völzke, G. Bosc-Bierne, C. Prinz, T. Schwaar, P. Andrle, H. Bäßler, K. Nguyen, Y. Zhu, A.S.J.S. Mey, A. Mostafa, I. Bald, M. G. Weller
    Viruses, 15(3), 697 (2023)
  18. Chapter 8: The IMAGINARY Journey to Open Mathematics Engagement: History and Current Projects
    19. E. Londaits, A. Matt, A.S.J.S. Mey, D. Ramos, C. Stussak, B. Violet
    World Scientific Series on Science Communication pp. 135-163 (2023)
  19. Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks
    20. D.F. Hahn, C.I. Bayly, H.E. Bruce Macdonald, J.D. Chodera, A.S.J.S. Mey, D.L. Mobley, L. Perez Benito, C.E. M. Schindler, G.Tresadern, G.L. Warren
    Living J. Comp. Mol. Sci. 4(1), 1497 (2022)
  20. Dynamic Profiling of β-Coronavirus 3CL Mpro Protease Ligand-Binding Sites
    21. E. Cho, M. Rosa, R. Anjum, S. Mehmood, M. Soban, M. Mujtaba, K. Bux, S. C. Dantu, A. Pandini, J. Yin, H. Ma, A. Ramanathan, B. Islam, A.S.J.S. Mey, D. Bhowmik, S. Haider
    J. Chem. Inf. Model. 61, 6, 3058–3073 (2021)
  21. Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free-Energy Calculations
    22. L. Nelson, S. Bariami, C. Ringrose, J. Horton, V. Kurdekar, A.S.J.S. Mey, J. Michel, D. Cole
    J. Chem. Inf. Model. 61, 5, 2124–2130 (2021)
  22. Best Practices for Alchemical Free Energy Calculations
    23. A.S.J.S. Mey, B. Allen, H.E. Bruce Macdonald, J.D. Chodera, D. Hahn, M. Kuhn, J. Michel, D.L. Mobley, L.N. Naden, S. Prasad, A. Rizzi, J. Scheen, M.R. Shirts, G. Tresadern, H. Xu
    Living J. Comp. Mol. Sci. 2 (1), 18378 (2020)
  23. A Hybrid Alchemical Free Energy and Machine Learning Methodology for the Calculation of Absolute Hydration Free Energies of Small Molecules
    24. J. Scheen, W. Wu, A.S.J.S. Mey, P. Tosco, M. Mackey, J. Michel
    J. Chem. Inf. Model. 60, 11, 5331–5339 (2020)
  24. Assessment of Binding Affinity via Alchemical Free-Energy Calculations
    25. M. Kuhn, S. Firth-Clark, P. Tosco, A.S.J.S. Mey, M. Mackey, J. Michel
    J. Chem. Inf. Model. 60, 6, 3120–3130 (2020)
  25. Self-organized emergence of folded protein-like network structures from geometric constraints
    26. N. Molkenthin, S. Mühle, A.S.J.S. Mey, M. Timme
    PLoS ONE 15(2), e0229230 (2020)
  26. Dynamic design: manipulation of millisecond timescale motions on the energy landscape of Cyclophilin A
    27. J. Juárez-Jiménez, A. Gupta, G. Karunanithy, A.S.J.S. Mey, et al.
    Chem. Sci., 11, 2670-2680 (2020)
  27. BioSimSpace: An interoperable Python framework for biomolecular simulation
    28. L.O. Hedges, A.S.J.S. Mey, C.A. Laughton, et al.
    JOSS, 4, 1831 (2019)
  28. Allosteric effects in a catalytically impaired variant of the enzyme Cyclophilin A may be explained by changes in nano-microsecond time scale motions
    29. P. Wapeesittipan, A.S.J.S. Mey, M. Walkinshaw, J. Michel
    Comms. Chem. 2 41 (2019)
  29. Effect of automation on the accuracy of alchemical free energy calculation protocols over a set of ACK1 inhibitors
    30. J.M. Granadino-Roldan*, A.S.J.S. Mey*, J.J. Perez, S. Bosisio, J. Rubio-Martinez, J. Michel
    PloS One 14, e0213217 (2019)
  30. Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations
    31. A.S.J.S. Mey, J. Juárez-Jiménez, J. Michel
    J. Comput. Aided. Mol. Des. 32, 199 (2018)
  31. Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge
    32. S. Bosisio, A.S.J.S. Mey, J. Michel,
    J. Comput. Aided. Mol. Des. 31, 61 (2017)
  32. Analytical methods for structural ensembles and dynamics of intrinsically disordered proteins
    33. M. Schor, A.S.J.S. Mey, C.E. MacPhee
    Biophys. Rev. 8, 429 (2016)
  33. Blinded predictions of distribution coefficients in the SAMPL5 challenge
    34. S. Bosisio, A.S.J.S. Mey, J. Michel
    J. Comput. Aided. Mol. Des. 30, 1101 (2016)
  34. Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R Grand Challenge
    35. A.S.J.S. Mey*, J. Juárez-Jiménez*, A. Hennessy, J. Michel
    Bioorg. Med. Chem. 24, 4890 (2016)
  35. Elucidation of Non-Additive Effects in Protein-Ligand Binding Energies: Thrombin as a Case Study,
    36. G. Calabrò, C.J. Woods, F. Powlesland, A.S.J.S Mey, A.J. Mulholland, J. Michel
    J. Phys. Chem. B 120, 5340 (2016)
  36. Shedding light on the dock-lock mechanism in amyloid fibril growth using Markov State Models
    37. M. Schor, A.S.J.S. Mey, F. Noé, C.E. MacPhee
    J. Phys. Chem. Lett. 6, 1076 (2015)
  37. Dynamic Properties of Forcefields
    38. F. Vitalini*, A.S.J.S. Mey*, F. Noé and B.G. Keller
    J. Chem. Phys. 142, 084101 (2015)
  38. Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states
    39. H. Wu, A.S.J.S. Mey, E. Rosta, F. Noé
    J. Chem. Phys. 141, 214106 (2014)
  39. xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states
    40. A.S.J.S. Mey, H. Wu, and F. Noé
    Phys. Rev. X 4, 041018 (2014)
  40. Rare-event trajectory ensemble analysis reveals metastable dynamical phases in lattice proteins
    41. A.S.J.S. Mey, P.L. Geissler and J.P. Garrahan
    Phys. Rev. E 89, 032109 (2014)
  41. Variational approach to molecular kinetics
    42. F. Nüske, B.G. Keller, G. Pérez-Hernández, A.S.J.S. Mey, F. Noé
    J. Chem. Theory Comput. 10, 1739 (2014)
  42. EMMA - A software package for Markov model building and analysis
    43. M. Senne, B. Trendelkamp-Schroer, A.S.J.S. Mey, C. Schütte, F. Noé
    J. Chem. Theory Comput. 8, 2223 (2012)
  43. Thermodynamics of trajectories of the one-dimensional Ising model
    44. E.S. Loscar, A.S.J.S. Mey, J.P. Garrahan
    J. Stat. Mech. 2011, P12011 (2011)