[1] S.-Y. Huang, X. Zou, Advances and challenges in protein-ligand docking, Int. J. Mol. Sci. 11 (2010) 3016-3034.
DOI: https://doi.org/10.3390/ijms11083016
[2] J. Fan, A. Fu, L. Zhang, Progress in molecular docking, Quant. Biol. 7 (2019) 83-89.
DOI: https://doi.org/10.1007/s40484-018-0163-4
[3] P.H. Torres, A.C. Sodero, P. Jofily, F.P. Silva-Jr, Key topics in molecular docking for drug design, Int. J. Mol. Sci. 20 (2019)
4574.
DOI: https://doi.org/10.3390/ijms20184574
[4] I.D. Kuntz, J.M. Blaney, S.J. Oatley, R. Langridge, T.E. Ferrin, A geometric approach to macromolecule-ligand interactions, J.
Mol. Biol. 161 (1982) 269-288.
DOI: https://doi.org/10.1016/0022-2836(82)90153-X
[5] E. Lionta, G. Spyrou, D.K. Vassilatis, Z. Cournia, Structure-based virtual screening for drug discovery: principles, applications
and recent advances, Curr. Top. Med. Chem. 14 (2014) 1923-1938.
DOI:
https://doi.org/10.2174/1568026614666140929124445
[6] L.G. Ferreira, R.N. Dos Santos, G. Oliva, A.D. Andricopulo, Molecular docking and structure-based drug design strategies,
Molecules 20 (2015) 13384-13421.
DOI: https://doi.org/10.3390/molecules200713384
[7] P. Śledź, A. Caflisch, Protein structure-based drug design: from docking to molecular dynamics, Curr. Opin. Struct. Biol. 48
(2018) 93-102.
DOI: https://doi.org/10.1016/j.sbi.2017.10.010
[8] X. Lin, X. Li, X. Lin, A review on applications of computational methods in drug screening and design, Molecules 25 (2020)
1375.
DOI: https://doi.org/10.3390/molecules25061375
[9] S.S. Bhunia, M. Saxena, A.K. Saxena, Ligand- and structure-based virtual screening in drug discovery, in: A. Saxena (Ed.),
Biophysical and Computational Tools in Drug Discovery, Springer, Cham, 2021, pp. 281-339.
DOI: https://doi.org/10.1007/7355_2021_128
[10] G. Klebe, Protein–ligand interactions as the basis for drug action, in: Drug Design: From Structure and Modeof-Action to Rational Design Concepts, Springer, Berlin, Heidelberg, 2025, pp. 39–65. ISBN: 978-3-662-70235-
2 (eBook).
[11] D. Koshland, Jr., Correlation of Structure and Function in Enzyme Action: Theoretical and experimental tools are leading to
correlations between enzyme structure and function, Science 142 (1963) 1533-1541.
DOI: https://doi.org/10.1126/science.142.3599.1533
[12] M. van den Noort, M. de Boer, B. Poolman, Stability of ligand-induced protein conformation influences affinity in maltosebinding protein, J. Mol. Biol. 433 (2021) 167036.
DOI: https://doi.org/10.1016/j.jmb.2021.167036
[13] V.B. Sulimov, D.C. Kutov, A.V. Sulimov, Advances in docking, Curr. Med. Chem. 26 (2019) 7555-7580.
DOI: https://doi.org/10.2174/0929867326666181203122542
[14] B. Waszkowycz, D.E. Clark, E. Gancia, Outstanding challenges in protein–ligand docking and structure‐based virtual
screening, Wiley Interdiscip. Rev. Comput. Mol. Sci. 1 (2011) 229-259.
DOI: https://doi.org/10.1002/wcms.18
[15] Y. Yan, M. Yang, C.G. Ji, J.Z. Zhang, Interaction entropy for computational alanine scanning, J. Chem. Inf. Model. 57 (2017)
1112-1122.
DOI: https://doi.org/10.1021/acs.jcim.7b00025
[16] X. Liu, L. Peng, Y. Zhou, Y. Zhang, J.Z. Zhang, Computational alanine scanning with interaction entropy for protein–ligand
binding free energies, J. Chem. Theory. Comput. 14 (2018) 1772-1780.
DOI: https://doi.org/10.1021/acs.jctc.8b00026
[17] H. Sun, L. Duan, F. Chen, H. Liu, Z. Wang, P. Pan, F. Zhu, J.Z. Zhang, T. Hou, Assessing the performance of MM/PBSA and
MM/GBSA methods. 7. Entropy effects on the performance of end-point binding free energy calculation approaches, Phys.
Chem. Chem. Phys. 20 (2018) 14450-14460.
DOI: https://doi.org/10.1039/C7CP07623A
[18] E. Wang, H. Sun, J. Wang, Z. Wang, H. Liu, J.Z. Zhang, T. Hou, End-point binding free energy calculation with MM/PBSA
and MM/GBSA: strategies and applications in drug design, Chem. Rev. 119 (2019) 9478-9508.
DOI: https://doi.org/10.1021/acs.chemrev.9b00055
[19] L. Wang, J. Chambers, R. Abel, Protein–ligand binding free energy calculations with FEP+, in: M. Bonomi, C. Camilloni
(Eds.), Biomolecular Simulations: Methods and Protocols, Humana Press, New York, 2019, pp. 201-232.
DOI:
https://doi.org/10.1007/978-1-4939-9608-7_9
[20] P.V. Rusina, I.Y. Titov, M.V. Panova, V.S. Stroylov, Y.R. Abdyusheva, E.Y. Murlatova, I.V. Svitanko, F.N. Novikov,
Modeling of novel CDK7 inhibitors activity by molecular dynamics and free energy perturbation methods, Mendeleev
Commun. 30 (2020) 430-432.
DOI: https://doi.org/10.1016/j.mencom.2020.07.002
[21] L.M. Mihalovits, G.G. Ferenczy, G.M. Keserű, Affinity and selectivity assessment of covalent inhibitors by free energy
calculations, J. Chem. Inf. Model. 60 (2020) 6579-6594.
DOI: https://doi.org/10.1021/acs.jcim.0c01067
[22] J. Liu, R. Wang, Classification of current scoring functions, J. Chem. Inf. Model. 55 (2015) 475-482.
DOI: https://doi.org/10.1021/ci500731a
[23] J. Li, A. Fu, L. Zhang, An overview of scoring functions used for protein–ligand interactions in molecular
docking, Interdiscip. Sci.: Comput. Life Sci. 11 (2019) 320–328.
DOI: https://doi.org/10.1007/s12539-019-00327-w
[24] M. Su, Q. Yang, Y. Du, G. Feng, Z. Liu, Y. Li, R. Wang, Comparative assessment of scoring functions: the CASF-2016
update, J. Chem. Inf. Model. 59 (2018) 895-913.
DOI: https://doi.org/10.1021/acs.jcim.8b00545
[25] O. Méndez-Lucio, M. Ahmad, E.A. del Rio-Chanona, J.K. Wegner, A geometric deep learning approach to predict binding
conformations of bioactive molecules, Nat. Mach. Intell. 3 (2021) 1033-1039.
DOI:
https://doi.org/10.1038/s42256-021-00409-9
[26] T. Harren, T. Gutermuth, C. Grebner, G. Hessler, M. Rarey, Modern machine‐learning for binding affinity estimation of
protein–ligand complexes: Progress, opportunities, and challenges, Wiley Interdiscip. Rev. Comput. Mol. Sci. 14 (2024) e1716.
DOI: https://doi.org/10.1002/wcms.1716
[27] K. Abbasi, P. Razzaghi, A. Poso, S. Ghanbari-Ara, A. Masoudi-Nejad, Deep learning in drug target interaction prediction:
current and future perspectives, Curr. Med. Chem. 28 (2021) 2100-2113.
DOI: https://doi.org/10.2174/0929867327666200908145101
[28] D.P. Kiouri, G.C. Batsis, C.T. Chasapis, Structure-Based Approaches for Protein–Protein Interaction Prediction Using
Machine Learning and Deep Learning, Biomolecules 15 (2025) 141.
DOI: https://doi.org/10.3390/biom15010141
[29] R. Chen, Z. Weng, A novel shape complementarity scoring function for protein‐protein docking, Proteins: Struct. Funct.
Bioinf. 51 (2003) 397-408.
DOI: https://doi.org/10.1002/prot.10334
[30] A. Shirali, V. Stebliankin, U. Karki, J. Shi, P. Chapagain, G. Narasimhan, A comprehensive survey of scoring functions for
protein docking models, BMC Bioinf. 26 (2025) 25.
DOI: https://doi.org/10.1186/s12859-024-05869-5
[31] D. Fischer, S.L. Lin, H.L. Wolfson, R. Nussinov, A geometry-based suite of molecular docking processes, J. Mol. Biol. 248
(1995) 459-477.
DOI: https://doi.org/10.1006/jmbi.1995.0235
[32] J. Rahman, M.H. Newton, M.E. Ali, A. Sattar, Distance plus attention for binding affinity prediction, J. Cheminform. 16
(2024) 52.
DOI: https://doi.org/10.1186/s13321-024-00854-9
[33] J. Gabel, J. Desaphy, D. Rognan, Beware of Machine Learning-Based Scoring Functions: On the Danger of Developing Black
Boxes, J. Chem. Inf. Model. 54 (2014) 2807-2815.
DOI: https://doi.org/10.1021/ci500406k
[34] C. Rudin, C. Chen, Z. Chen, H. Huang, L. Semenova, C. Zhong, Interpretable machine learning: Fundamental principles and
10 grand challenges, Stat. Surv. 16 (2022) 1-85.
DOI: https://doi.org/10.1214/21-SS133
[35] D.E. Uehling, B. Joseph, K.C. Chung, A.X. Zhang, S. Ler, M.A. Prakesch, G. Poda, J. Grouleff, A. Aman, T. Kiyota, Design,
synthesis, and characterization of 4-aminoquinazolines as potent inhibitors of the G protein-coupled receptor kinase 6 (GRK6)
for the treatment of multiple myeloma, J. Med. Chem. 64 (2021) 11129-11147.
DOI: https://doi.org/10.1021/acs.jmedchem.1c00192
[36] C.A. Boguth, P. Singh, C.C. Huang, J.J. Tesmer, Molecular basis for activation of G protein‐coupled receptor kinases, EMBO
J. 29 (2010) 3249-3259.
DOI: https://doi.org/10.1038/emboj.2010.206
[37] K.T. Homan, J.J. Tesmer, Molecular basis for small molecule inhibition of G protein-coupled receptor kinases, ACS Chem.
Biol. 10 (2015) 246-256. DOI: https://doi.org/10.1021/cb5003163
[38] S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B.A. Shoemaker, P.A. Thiessen, B. Yu, PubChem in 2021: new
data content and improved web interfaces, Nucleic Acids Res. 49 (2021) D1388-D1395.
DOI: https://doi.org/10.1093/nar/gkaa971
[39] D.A. Evans, History of the Harvard ChemDraw project, Angew. Chem. Int. Ed. Engl. 53 (2014) 11140-11145.
DOI: https://doi.org/10.1002/anie.201405820
[40] J.J. Stewart, Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application
to 70 elements, J. Mol. Model. 13 (2007) 1173-1213.
[41] N.M. O'Boyle, M. Banck, C.A. James, C. Morley, T. Vandermeersch, G.R. Hutchison, Open Babel: An open chemical toolbox,
J. Cheminform. 3 (2011) 1-14.
DOI: https://doi.org/10.1186/1758-2946-3-33
[42] O. Trott, A.J. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient
optimization, and multithreading, J. Comput. Chem. 31 (2010) 455-461.
DOI: https://doi.org/10.1002/jcc.21334
[43] G.M. Morris, R. Huey, W. Lindstrom, M.F. Sanner, R.K. Belew, D.S. Goodsell, A.J. Olson, AutoDock4 and AutoDockTools4:
Automated docking with selective receptor flexibility, J. Comput. Chem. 30 (2009) 2785-2791.
DOI: https://doi.org/10.1002/jcc.21256
[44] A.L. Hopkins, G.M. Keserü, P.D. Leeson, D.C. Rees, C.H. Reynolds, The role of ligand efficiency metrics in drug discovery,
Nat. Rev. Drug. Discov. 13 (2014) 105-121.
DOI: https://doi.org/10.1038/nrd4163
[45] X. Pan, H. Wang, Y. Zhang, X. Wang, C. Li, C. Ji, J.Z. Zhang, AA-score: a new scoring function based on amino acid-specific
interaction for molecular docking, J. Chem. Inf. Model. 62 (2022) 2499-2509.
DOI: https://doi.org/10.1021/acs.jcim.2c00050
[46] C. Spearman, The proof and measurement of association between two things, in: J.J. Jenkins, D.G. Paterson (Eds.), Studies in
individual differences: The search for intelligence, Appleton-Century-Crofts, New York, 1961, pp. 45-55. DOI not available.
[47] W.R. Knight, A computer method for calculating Kendall's tau with ungrouped data, J. Am. Stat. Assoc. 61 (1966) 436-439.
DOI: https://doi.org/10.1080/01621459.1966.10480879
[48] A. Lee, K. Lee, D. Kim, Using reverse docking for target identification and its applications for drug discovery, Expert Opin.
Drug. Discov. 11 (2016) 707-715.
DOI: https://doi.org/10.1080/17460441.2016.1190706
[49] X. Xu, M. Huang, X. Zou, Docking-based inverse virtual screening: methods, applications, and challenges, Biophys. Rep. 4
(2018) 1-16.
DOI: https://doi.org/10.1007/s41048-017-0045-8
[50] J. Caballero, The latest automated docking technologies for novel drug discovery, Expert Opin. Drug. Discov. 16 (2021) 625-
645.
DOI: https://doi.org/10.1080/17460441.2021.1918093