Publications

Preprints:

1. Fluorophore design via generative modeling in a curated subspace, in revision

2. A Generative Data-Augmented Pipeline for Robust Immunoglobulin-Antigen Binding Prediction, submitted

3. in silico Screening based KLHL41 Ligand Discovery for Muscle Cell Specific Protein Degradation, submitted

4. Large Language Models Pass the Korean Pharmacist Licensing Examination: A Benchmarking Study, submitted

5. Ko, J. and Lee, J.* Can AlphaFold predict protein-peptide binding accurately? https://www.biorxiv.org/content/10.1101/2021.07.27.453972v2

Published:

1. Ucak U., Ashyrmamatov, I., and Lee, J.* Refining EI-MS library search results through atomic-level insights, accepted in Communications Chemistry (2025)

2. Halo8: a dataset of chemical reaction pathways incorporating halogen chemistry, accepted in Scientific Data (2025)

3. A Survey on Large Language Models in Biology and Chemistry, accepted in Experimental & Molecular Medicine (2025)

4. Engineering Cytosol-Penetrating IgG Antibodies with Enhanced Endosomal Escape for Intracellular Targeting and Payload Delivery, J. Contr. Release 382, 10, 113727 (2025) [link]

5. Park, S.J., Cerella, C., Kang, J.M. et al. Tetrahydrobenzimidazole TMQ0153 targets OPA1 and restores drug sensitivity in AML via ROS-induced mitochondrial metabolic reprogramming. J Exp Clin Cancer Res 44, 114 (2025) [link]

6. Sim S., Kim, D., Kim, B., Choi, J. and Lee, J.*, Recent advances in AI-driven protein-ligand interaction predictions, Curr Opin Struc Biol. (2025) Vol. 92, 103020 [link]

7. Kim, M., Ucak U., Ashyrmamatov, I., Lee, J.* and Sim, E.*, Automated and Efficient Sampling of Chemical Reaction Space, Advanced Science (2025) 2409009 [link]

8. Eom, H. et al. Discovery of Highly Active Kynureninases for Cancer Immunotherapy through Protein Language Model, Nucleic Acids Research, 53, (1), 13 (2025) [link]

9. Kim, J. et al. Strategies to overcome hurdles in cancer immunotherapy, Biomaterials Research, (2024);28:0080 [link]

10. Song J., Ha, J., Lee, J.,Ko J., and Shin, W., Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases, Scientific Reports (2024) 14, 25167 [link]

11. Hong, Y. et al., Accurate Prediction of Protein-Ligand Interactions by Combining Physical Energy Functions and Graph-Neural Networks, J. Cheminfo. (2024) 16:121 [link]

12. Choi, SR., Lee, J.*, Seo, YJ. et al. Molecular basis of facilitated target search and sequence discrimination of TALE homeodomain transcription factor Meis1. Nat Commun 15, 6984 (2024). (link)

13. Yang, H., et al. Discovery of thiophen-2-ylmethylene bis-dimedone derivatives as novel WRN inhibitors for treating cancers with microsatellite instability Bioorganic & Medicinal Chemistry 100 (2024) 117588 (link)

14. Yi, J.H., et al., Aβ dissociation by pectolinarin may counteract against Aβ-induced synaptic dysfunction and memory impairment, Biochemical Pharmacology, 216, 115792 (2023)

15. Ashyrmamatov, I., Ucak, U., and Lee, J.*, Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization, J Cheminform 15, 55 (2023). https://doi.org/10.1186/s13321-023-00725-9

16. Byun, J., Vellampatti, S., Chatterjee, P., Hwang, S.H., Kim, B*. and Lee, J.*, Characterization of the role of KPI domain in the dimerization of amyloid precursor protein, J. Comput. Chem. (2023), 1. https://doi.org/10.1002/jcc.27100

17. Shim, J., Lee, J.*, and Yoo, J.*, Finding multiple transition pathways with QM potentials by combining the least action principle and Gaussian process, 2023 J. Phys. Commun. 7 025004. https://iopscience.iop.org/article/10.1088/2399-6528/acba83

18. Ucak, U., Ashyrmamatov, I., and Lee, J.*, Reconstruction of lossless molecular representations, SMILES, and SELFIES from fingerprints, J Cheminform 15, 26 (2023). https://doi.org/10.1186/s13321-023-00693-0

19. Lee, J., et al., N4-Phenyl quinazoline-4,6-diamine as a tunable fluorescent scaffold for the fluorescence probes, Dye and Pigments, 2023, vol. 210, 110987. https://doi.org/10.1016/j.dyepig.2022.110987

20. Shin, W., et al., S-Pred: Protein Structural Property Prediction Using MSA Transformer, Sci Rep., 2022, 12, 13891. https://doi.org/10.1038/s41598-022-18205-9

21. Nicholas, A. et al., Discovery of penta-peptides inhibiting activity of formylglycine generating enzyme and its potential antibacterial effect against Mycobacterium tuberculosis, RSC Adv. (2022) 12, 18884-18888. https://doi.org/10.1039/D2RA03379H

22. Shin, C. H., ..., Lee, J.Y.*, Lee, J.M.*, and Oh, S. J.*, Cytosolic microRNA-inducible nuclear translocation of CRISPR protein controls disease-specific genome modification in cancer, Nucleic Acids Res. (2022). https://doi.org/10.1093/nar/gkac431.

23. Lee, S.J.; Joo, K.; Sim, S.; Lee, J.Y.; Lee, I.-H.; Lee, J. CRFalign: A Sequence-Structure Alignment of Proteins Based on a Combination of HMM-HMM Comparison and Conditional Random Fields. Molecules 2022, 27, 3711. https://doi.org/10.3390/molecules27123711

24. Hong, Y., Lee, J.* & Ko, J. A-Prot: protein structure modeling using MSA transformer. BMC Bioinformatics 23, 93 (2022). https://doi.org/10.1186/s12859-022-04628-8

25. Chen, A., Lee, J.*, Damjanovic A.*, Brooks, BR.*, Protein pKa prediction by tree-based machine learning, J. Chem. Theory Comput. (2022) https://doi.org/10.1021/acs.jctc.1c01257. 

26. Ucak, U., Ashyrmamatov, I., Ko, J. and Lee, J.* RetroTRAE: retrosynthetic translation of atomic environments with Transformer, Nat Commun 13, 1186 (2022). https://doi.org/10.1038/s41467-022-28857-w

27. Kang, B., Lee, J.*, and Seok, C.*, A Benchmark Study of Machine Learning Methods for Molecular electronic transition: Tree-based Ensemble Learning versus Graph Neural Network, Bull. Kor. Chem. Soc. (2022) https://doi.org/10.1002/bkcs.12468

28. Byun, J. and Lee, J.*, Identifying the hot spot residues of the SARS-CoV-2 main protease using MM-PBSA and multiple force fields, Life (2022), 12(1), 54. https://doi.org/10.3390/life12010054

29. Choi, J., & Lee, J.* V-Dock: Fast Generation of Novel Drug-Like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization. Int. J. Mol. Sci, (2021) 22, 11635. https://doi.org/10.3390/ijms222111635

30. Kang, B., Seok, C., & Lee, J.* MOLGENGO: Finding Novel Molecules with Desired Electronic Properties by Capitalizing on Their Global Optimization. ACS Omega, (2021) 6(41), 27454–27465. https://doi.org/10.1021/acsomega.1c04347

31. Kwon, Y.; Lee, J.* MolFinder : an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES. J. Cheminform. (2021), 13, 24, doi:10.1186/s13321-021-00501-7.

32. Ucak, U.V., Kang, T., Ko, J. and Lee, J.* Substructure-based neural machine translation for retrosynthetic prediction. J Cheminform 13, 4 (2021). https://doi.org/10.1186/s13321-020-00482-z

33. Lee, J.* & Brooks, B. R. Direct global optimization of Onsager-Machlup action using Action-CSA. Chem. Phys. 535, 110768 (2020).

34. Kang, B., Seok, C. & Lee, J.* Prediction of Molecular Electronic Transitions Using Random Forests. J. Chem. Inf. Model. (2020). doi:10.1021/acs.jcim.0c00698

35. Kwon, Y., Shin, W. H., Ko, J. & Lee, J.* AK-score: Accurate protein-ligand binding affinity prediction using an ensemble of 3D-convolutional neural networks. Int. J. Mol. Sci. 21, 1–16 (2020).

36. Choobdar, S. et al. Assessment of network module identification across complex diseases. Nat. Methods 16, 843 (2019).

37. Cheng, Q., Joung, I., Lee, J., Kuwajima, K. & Lee, J. Exploring the Folding Mechanism of Small Proteins GB1 and LB1. J. Chem. Theory Comput. 15, 3432–3449 (2019).

38. Choi, E., Lee, J., Kwon, I. C. & Kim, S. Cumulative directional calcium gluing between phosphate and silicate: A facile, robust and biocompatible strategy for siRNA delivery by amine-free non-positive vector. Biomaterials 209, 126–137 (2019).

-Before KNU-

39. Lee, J., Zhang, Z.-Y., Lee, J., Brooks, B. R. & Ahn, Y.-Y. Inverse Resolution Limit of Partition Density and Detecting Overlapping Communities by Link-Surprise. Scientific Reports 7, 12399 (2017).

40. Lee, Juyong§,**, Konc, J.**, Janez, D., and Brooks, B.R. (2017) Global organization of a binding site network gives insight into evolution and structure-function relationships of proteins, Scientific Reports, 7: 11652, doi:10.1038/s41598-017-10412-z.

41. Lee, Juyong§, Lee, I.-H., Joung, I., Lee, J., and Brooks, B.R. (2017) Finding multiple reaction pathways via global optimization of action. Nature Communications 8, 15443, doi: 10.1038/ncomms15443.

42. Lee, Juyong§, et al. (2017) Absolute binding free energy calculations of CBClip host-guest systems in the SAMPL5 blind challenge, Journal of Computer-Aided Molecular Design, 31 (1), 71.

43. Tofoleanu, F., Lee, Juyong, and et al. (2017) Absolute binding free energies for octa-acids and guests, Journal of Computer-Aided Molecular Design, 31 (1), 107.

44. Lee, Juyong§, Miller, B.T., and Brooks, B.R. (2016) Computational scheme for pH-dependent binding free energy calculation with explicit solvent, Protein Science, 25, 231.

45. Heo, S., Lee, Juyong, Joo, K., Shin, H., and Lee, J. (2016) Protein loop structure prediction using conformational space annealing, Journal of Chemical Informatics and Modeling, doi: 10.1021/acs.jcim.6b00742. 

46. Wu, X., Lee, Juyong, and Brooks, B.R. (2016) Origin of pKa shifts of internal lysine residues in SNase studied via equal-molar VMMS simulations in explicit water, Journal of Physical Chemistry B, doi:10.1021/acs.jpcb.6b08249.

47. Pickard, F.C., et al. (2016) Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pKa corrections, Journal of Computer-Aided Molecular Design, 30 (11), 1087. 

48. König, G., et al. (2016) Calculating distribution coefficients based on multi-scale free energy simulations — An evaluation of MM and QM/MM explicit solvent simulations of water-cyclohexane transfer in the SAMPL5 challenge, Journal of Computer-Aided Molecular Design, 30 (11), 989. 

49. Joo, K. et al. (2015) Template based protein structure modeling by global optimization in CASP11. Proteins: Structure, Function, and Bioinformatics, 84 (Suppl 1), 221.

50. Lee, Juyong, Joo, K., Brooks, B.R., and Lee J. (2015) The atomistic mechanism of conformational transition of adenylate kinase investigated by Lorentzian structure-based potential, Journal of Chemical Theory and Computation, 11 (7), 3211.

51. Lee, Juyong§, Miller, B. T., Damjanovic, A., and Brooks, B.R. (2015) Enhancing constant-pH simulation in explicit solvent with a two-dimensional replica exchange method, Journal of Chemical Theory and Computation, 11 (6), 2560.

52. Lee, Juyong, Lee, K., Joo, K., Brooks, B.R., and Lee, J. (2015) Sigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest, BMC Bioinformatics, 16 (1), 94.

53. Lee, Juyong§, Miller, B. T., Damjanovic, A., and Brooks. B.R. (2014) Constant pH Molecular Dynamics in Explicit Solvent with Enveloping Distribution Sampling and Hamiltonian Exchange, Journal of Chemical Theory Computation, 10 (7), 2738.

54. Manavalan, B., Lee, Juyong, and Lee, J. (2014) Random Forest-Based Protein Model Quality Assessment (RFMQA) using structural features and potential energy terms, PLOS ONE, 9 (9), e106542.

55. Malik, A., Lee, Juyong, and Lee, J. (2014) Community-Based Network Study of Protein-Carbohydrate Interactions in Plant Lectins Using Glycan Array Data, PLOS ONE 9 (4): e95480.

56. Joo, K., Lee, Juyong, and et al. (2014) Protein structure modeling for CASP10 by multiple layers of global optimization, Proteins: Structure. Function, and Bioinformatics, 82 (Suppl 2), 188.

57. Lee, Juyong, Gross, S. and Lee, J. (2013) Improved network community structure improves function prediction, Scientific Reports 3:02197.

58. Lee, Juyong and Lee, J. (2013) Hidden information revealed by optimal community structure from a protein-complex bipartite network improves protein function prediction, PLOS ONE 8 (4): e60372.

59. Lee, DS., Lee, Juyong and Seok, C. (2013) What stabilizes close arginine pairing in proteins?, Physical Chemistry Chemical Physics, 15 (16), 5844.

60. Lee, Y., Lee, Juyong, Choi, J., and Seok, C. (2012) Contribution of counterion entropy to the salt-induced transition between B-DNA and Z-DNA, Bulletin of the Korean Chemical Society, 33 (11), 3719.

61. Lee, Juyong, Gross, S. and Lee, J. (2012) Modularity optimization by conformational space annealing, Physical Review E 85, 056702. 

62. Lee, Juyong, Lee, J., Sasaki, T., Sasai, M., Seok, C. and Lee, J. (2011) De novo protein structure prediction using fragment based potential and conformational space annealing, Proteins: Structure, Function, and Bioinformatics, 79 (8), 2403.

63. Shin, W., Lim, H., Lee, Juyong, Ko, J., Seok, C. and Lee, J. (2011) LigDockCSA: protein-ligand docking using conformational space annealing, Journal of Computational Chemistry, 32 (15), 3226.

64. Lee, Juyong, Kim, YG., Kim, KK., and Seok, C. (2010) Transition between B-DNA and Z-DNA: free energy landscape for the B-Z junction propagation, Journal of Physical Chemistry B, 114 (30), 9872.

65. Lee, Juyong, Kim, JS., and Seok, C. (2010) Cooperativity and Specificity of Cys2His2 Zinc Finger Protein−DNA Interactions: A Molecular Dynamics Simulation Study, Journal of Physical Chemistry B, 114 (22), 7662. 

66. Lee, Juyong**, Kang, S.**, Lee, MS. and Seok, C. (2008) Structural basis of functional conversion of a floral repressor to an activator, Bulletin of the Korean Chemical Society, 29, 408. 

67. Lee, Juyong and Seok, C. (2007) A statistical rescoring scheme for protein-ligand docking: Consideration of entropic effect, Proteins: Structure. Function, and Bioinformatics, 70 (3), 1074.