Skip to content
2000
Volume 32, Issue 28
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Background

Computational assessment of the energetics of protein-ligand complexes is a challenge in the early stages of drug discovery. Previous comparative studies on computational methods to calculate the binding affinity showed that targeted scoring functions outperform universal models.

Objective

The goal here is to review the application of a simple physics-based model to estimate the binding. The focus is on a mass-spring system developed to predict binding affinity against cyclin-dependent kinase.

Methods

Publications in PubMed were searched to find mass-spring models to predict binding affinity. Crystal structures of cyclin-dependent kinases found in the protein data bank and two web servers to calculate affinity based on the atomic coordinates were employed.

Results

One recent study showed how a simple physics-based scoring function (named Taba) could contribute to the analysis of protein-ligand interactions. Taba methodology outperforms robust physics-based models implemented in docking programs such as AutoDock4 and Molegro Virtual Docker. Predictive metrics of 27 scoring functions and energy terms highlight the superior performance of the Taba scoring function for cyclin-dependent kinase.

Conclusion

The recent progress of machine learning methods and the availability of these techniques through free libraries boosted the development of more accurate models to address protein-ligand interactions. Combining a naïve mass-spring system with machine-learning techniques generated a targeted scoring function with superior predictive performance to estimate pK.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673307315240730042209
2024-08-01
2025-09-06

  • From This Site

    /content/journals/cmc/10.2174/0109298673307315240730042209
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. ZhaoL. ZhuY. WangJ. WenN. WangC. ChengL. A brief review of protein–ligand interaction prediction.Comput. Struct. Biotechnol. J.2022202831283810.1016/j.csbj.2022.06.00435765652
     [Google Scholar]
  2. ZhaoJ. CaoY. ZhangL. Exploring the computational methods for protein-ligand binding site prediction.Comput. Struct. Biotechnol. J.20201841742610.1016/j.csbj.2020.02.00832140203
     [Google Scholar]
  3. BöhmH.J. A novel computational tool for automated structure-based drug design.J. Mol. Recognit.19936313113710.1002/jmr.3000603058060670
     [Google Scholar]
  4. Mena-UleciaK. TiznadoW. CaballeroJ. Study of the differential activity of thrombin inhibitors using docking, QSAR, molecular dynamics, and MM-GBSA.PLoS One20151011e014277410.1371/journal.pone.014277426599107
     [Google Scholar]
  5. GohlkeH. HendlichM. KlebeG. Knowledge-based scoring function to predict protein-ligand interactions.J. Mol. Biol.2000295233735610.1006/jmbi.1999.337110623530
     [Google Scholar]
  6. ShaC.M. WangJ. DokholyanN.V. NeuralDock: Rapid and conformation-agnostic docking of small molecules.Front. Mol. Biosci.2022986724110.3389/fmolb.2022.86724135392534
     [Google Scholar]
  7. EberhardtJ. Santos-MartinsD. TillackA.F. ForliS. AutoDock vina 1.2.0: New docking methods, expanded force field, and python bindings.J. Chem. Inf. Model.20216183891389810.1021/acs.jcim.1c0020334278794
     [Google Scholar]
  8. MorrisG.M. HueyR. LindstromW. SannerM.F. BelewR.K. GoodsellD.S. OlsonA.J. AutoDock4 and autodocktools4: Automated docking with selective receptor flexibility.J. Comput. Chem.200930162785279110.1002/jcc.2125619399780
     [Google Scholar]
  9. ThomsenR. ChristensenM.H. MolDock: A new technique for high-accuracy molecular docking.J. Med. Chem.200649113315332110.1021/jm051197e16722650
     [Google Scholar]
  10. Bitencourt-FerreiraG. de AzevedoW.F.Jr Molegro virtual docker for docking.Methods Mol. Biol.2019205314916710.1007/978‑1‑4939‑9752‑7_1031452104
     [Google Scholar]
  11. Vazquez-RodriguezS. Ramírez-ContrerasD. NoriegaL. García-GarcíaA. Sánchez-GaytánB.L. MelendezF.J. CastroM.E. de AzevedoW.F.Jr González-VergaraE. Interaction of copper potential metallodrugs with TMPRSS2: A comparative study of docking tools and its implications on COVID-19.Front Chem.202311112885910.3389/fchem.2023.112885936778030
     [Google Scholar]
  12. SpyrakisF. AmadasiA. FornabaioM. AbrahamD.J. MozzarelliA. KelloggG.E. CozziniP. The consequences of scoring docked ligand conformations using free energy correlations.Eur. J. Med. Chem.200742792193310.1016/j.ejmech.2006.12.03717346861
     [Google Scholar]
  13. BoylesF. DeaneC.M. MorrisG.M. Learning from the ligand: Using ligand-based features to improve binding affinity prediction.Bioinformatics202036375876410.1093/bioinformatics/btz66531598630
     [Google Scholar]
  14. WójcikowskiM. SiedleckiP. BallesterP.J. Building machine-learning scoring functions for structure-based prediction of intermolecular binding affinity.Methods Mol. Biol.2019205311210.1007/978‑1‑4939‑9752‑7_131452095
     [Google Scholar]
  15. Bitencourt-FerreiraG. Veit-AcostaM. de AzevedoW.F.Jr Electrostatic energy in protein–ligand complexes.Methods Mol. Biol.20192053677710.1007/978‑1‑4939‑9752‑7_531452099
     [Google Scholar]
  16. Bitencourt-FerreiraG. de Azevedo JuniorW.F. Electrostatic potential energy in protein-drug complexes.Curr. Med. Chem.202128244954497110.2174/1875533XMTEzhODQlw33593246
     [Google Scholar]
  17. Bitencourt-FerreiraG. Veit-AcostaM. de AzevedoW.F.Jr Van der waals potential in protein complexes.Methods Mol. Biol.20192053799110.1007/978‑1‑4939‑9752‑7_631452100
     [Google Scholar]
  18. Bitencourt-FerreiraG. Veit-AcostaM. de AzevedoW.F.Jr Hydrogen bonds in protein-ligand complexes.Methods Mol. Biol.201920539310710.1007/978‑1‑4939‑9752‑7_731452101
     [Google Scholar]
  19. ShinW.H. HeoL. LeeJ. KoJ. SeokC. LeeJ. LigDockCSA: Protein–ligand docking using conformational space annealing.J. Comput. Chem.201132153226323210.1002/jcc.2190521837636
     [Google Scholar]
  20. ForliS. OlsonA.J. A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking.J. Med. Chem.201255262363810.1021/jm200514522148468
     [Google Scholar]
  21. BredaA. BassoL. SantosD. de AzevedoW.Jr Virtual screening of drugs: Score functions, docking, and drug design.Curr. Computeraided Drug Des.20084426527210.2174/157340908786786047
     [Google Scholar]
  22. SantosL.H.S. FerreiraR.S. CaffarenaE.R. Integrating molecular docking and molecular dynamics simulations.Methods Mol. Biol.20192053133410.1007/978‑1‑4939‑9752‑7_231452096
     [Google Scholar]
  23. RossG.A. MorrisG.M. BigginP.C. One size does not fit all: The limits of structure-based models in drug discovery.J. Chem. Theory Comput.2013994266427410.1021/ct400422824124403
     [Google Scholar]
  24. LiJ. FuA. ZhangL. An overview of scoring functions used for protein–ligand interactions in molecular docking.Interdiscip. Sci.201911232032810.1007/s12539‑019‑00327‑w30877639
     [Google Scholar]
  25. da SilvaA.D. Bitencourt-FerreiraG. de AzevedoW.F.Jr Taba: A tool to analyze the binding affinity.J. Comput. Chem.2020411697310.1002/jcc.2604831410856
     [Google Scholar]
  26. GoelG. ChouI.C. VoitE.O. Biological systems modeling and analysis: A biomolecular technique of the twenty-first century.J. Biomol. Tech.200617425226917028166
     [Google Scholar]
  27. KitanoH. Computational systems biology.Nature2002420691220621010.1038/nature0125412432404
     [Google Scholar]
  28. MarkowetzF. All biology is computational biology.PLoS Biol.2017153e200205010.1371/journal.pbio.200205028278152
     [Google Scholar]
  29. WesterhoffH.V. PalssonB.O. The evolution of molecular biology into systems biology.Nat. Biotechnol.200422101249125210.1038/nbt102015470464
     [Google Scholar]
  30. Bitencourt-FerreiraG. VillarrealM.A. QuirogaR. BiziukovaN. PoroikovV. TarasovaO. de Azevedo JuniorW.F. Exploring scoring function space: Developing computational models for drug discovery.Curr. Med. Chem.202431172361237710.2174/092986733066623032110373136944627
     [Google Scholar]
  31. Bitencourt-FerreiraG. de AzevedoW.F.Jr Molecular dynamics simulations with NAMD2.Methods Mol. Biol.2019205310912410.1007/978‑1‑4939‑9752‑7_831452102
     [Google Scholar]
  32. de AzevedoW.F.Jr Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr. Med. Chem.20111891353136610.2174/09298671179502951921366529
     [Google Scholar]
  33. SulimovV.B. KutovD.C. SulimovA.V. Advances in docking.Curr. Med. Chem.202026427555758010.2174/092986732566618090411500030182836
     [Google Scholar]
  34. Veit-AcostaM. de Azevedo JuniorW.F. The impact of crystallographic data for the development of machine learning models to predict protein-ligand binding affinity.Curr. Med. Chem.202128347006702210.2174/092986732866621021012132033568025
     [Google Scholar]
  35. QuirogaR. VillarrealM.A. Vinardo: A scoring function based on autodock vina improves scoring, docking, and virtual screening.PLoS One2016115e015518310.1371/journal.pone.015518327171006
     [Google Scholar]
  36. BohacekR.S. McMartinC. GuidaW.C. The art and practice of structure-based drug design: A molecular modeling perspective.Med. Res. Rev.199616135010.1002/(SICI)1098‑1128(199601)16:1<3::AID‑MED1>3.0.CO;2‑68788213
     [Google Scholar]
  37. Maynard SmithJ. Natural selection and the concept of a protein space.Nature1970225523256356410.1038/225563a05411867
     [Google Scholar]
  38. GuzenkoD. BurleyS.K. DuarteJ.M. Real time structural search of the protein data bank.PLOS Comput. Biol.2020167e100797010.1371/journal.pcbi.100797032639954
     [Google Scholar]
  39. GilsonM.K. LiuT. BaitalukM. NicolaG. HwangL. ChongJ. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology.Nucleic Acids Res.201644D1D1045D105310.1093/nar/gkv107226481362
     [Google Scholar]
  40. WagleS. SmithR.D. DominicA.J.III DasGuptaD. TripathiS.K. CarlsonH.A. Sunsetting binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools.Sci. Rep.2023131300810.1038/s41598‑023‑29996‑w36810894
     [Google Scholar]
  41. LiuZ. LiY. HanL. LiJ. LiuJ. ZhaoZ. NieW. LiuY. WangR. PDB-wide collection of binding data: current status of the PDBbind database.Bioinformatics201531340541210.1093/bioinformatics/btu626
     [Google Scholar]
  42. PedregosaF. VaroquauxG. GramfortA. MichelV. ThirionB. GriselO. BlondelM. PrettenhoferP. WeissR. DubourgV. VerplasJ. PassosA. CournapeauD. BrucherM. PerrotM. DuchesnayE. Scikitlearn: Machine learning in python.J. Mach. Learn. Res.20111228252830
     [Google Scholar]
  43. de AzevedoW.F. Application of machine learning techniques for drug discovery.Curr. Med. Chem.202128387805780710.2174/09298673283821120715454934911417
     [Google Scholar]
  44. Bitencourt-FerreiraG. de AzevedoW.F.Jr Exploring the scoring function space.Methods Mol. Biol.2019205327528110.1007/978‑1‑4939‑9752‑7_1731452111
     [Google Scholar]
  45. Veit-AcostaM. de Azevedo JuniorW.F. Computational prediction of binding affinity for CDK2-ligand complexes. A protein target for cancer drug discovery.Curr. Med. Chem.202229142438245510.2174/092986732866621080610581034365938
     [Google Scholar]
  46. VeríssimoG.C. SerafimM.S.M. KronenbergerT. FerreiraR.S. HonorioK.M. MaltarolloV.G. Designing drugs when there is low data availability: One-shot learning and other approaches to face the issues of a long-term concern.Expert Opin. Drug Discov.202217992994710.1080/17460441.2022.211445135983695
     [Google Scholar]
  47. MurrayA.W. Cyclin-dependent kinases: Regulators of the cell cycle and more.Chem. Biol.19941419119510.1016/1074‑5521(94)90009‑49383389
     [Google Scholar]
  48. MorganD.O. Principles of CDK regulation.Nature1995374651813113410.1038/374131a07877684
     [Google Scholar]
  49. MalumbresM. Cyclin-dependent kinases.Genome Biol.201415612210.1186/gb418425180339
     [Google Scholar]
  50. BártekJ. StaškováZ. DraettaG. LukášJ. Molecular pathology of the cell cycle in human cancer cells.Stem Cells199311Suppl. 1515810.1002/stem.55301106118318919
     [Google Scholar]
  51. De BondtH.L. RosenblattJ. JancarikJ. JonesH.D. MorganD.O. KimS.H. Crystal structure of cyclin-dependent kinase 2.Nature1993363643059560210.1038/363595a08510751
     [Google Scholar]
  52. Schulze-GahmenU. De BondtH.L. KimS.H. High-resolution crystal structures of human cyclin-dependent kinase 2 with and without ATP: Bound waters and natural ligand as guides for inhibitor design.J. Med. Chem.199639234540454610.1021/jm960402a8917641
     [Google Scholar]
  53. XavierM.M. HeckG.S. AvilaM.B. LevinN.M.B. PintroV.O. CarvalhoN.L. AzevedoW.F.Jr SAnDReS a computational tool for statistical analysis of docking results and development of scoring functions.Comb. Chem. High Throughput Screen.2016191080181227686428
     [Google Scholar]
  54. Schulze-GahmenU. BrandsenJ. JonesH.D. MorganD.O. MeijerL. VeselyJ. KimS.H. Multiple modes of ligand recognition: Crystal structures of cyclin-dependent protein kinase 2 in complex with ATP and two inhibitors, olomoucine and isopentenyladenine.Proteins199522437839110.1002/prot.3402204087479711
     [Google Scholar]
  55. WoodD.J. KorolchukS. TatumN.J. WangL.Z. EndicottJ.A. NobleM.E.M. MartinM.P. Differences in the conformational energy landscape of CDK1 and CDK2 suggest a mechanism for achieving selective CDK inhibition.Cell Chem. Biol.2019261121130.e510.1016/j.chembiol.2018.10.01530472117
     [Google Scholar]
  56. MartinM.P. AlamR. BetziS. InglesD.J. ZhuJ.Y. SchönbrunnE. A novel approach to the discovery of small-molecule ligands of CDK2.ChemBioChem201213142128213610.1002/cbic.20120031622893598
     [Google Scholar]
  57. KryštofV. CankařP. FryšováI. SloukaJ. KontopidisG. DžubákP. HajdúchM. SrovnalJ. de AzevedoW.F.Jr OrságM. PaprskářováM. RolčíkJ. LátrA. FischerP.M. StrnadM. 4-arylazo-3,5-diamino-1H-pyrazole CDK inhibitors: SAR study, crystal structure in complex with CDK2, selectivity, and cellular effects.J. Med. Chem.200649226500650910.1021/jm060574017064068
     [Google Scholar]
  58. De AzevedoW.F. LeclercS. MeijerL. HavlicekL. StrnadM. KimS.H. Inhibition of cyclin-dependent kinases by purine analogues: Crystal structure of human cdk2 complexed with roscovitine.Eur. J. Biochem.19972431-251852610.1111/j.1432‑1033.1997.0518a.x9030780
     [Google Scholar]
  59. ShenJ. ZhangH. Function and structure of bradykinin receptor 2 for drug discovery.Acta Pharmacol. Sin.202344348949810.1038/s41401‑022‑00982‑836075965
     [Google Scholar]
  60. KluptK.A. JiaZ. eEF2K inhibitor design: The progression of exemplary structure-based drug design.Molecules2023283109510.3390/molecules2803109536770760
     [Google Scholar]
  61. DorahyG. ChenJ.Z. BalleT. Computer-aided drug design towards new psychotropic and neurological drugs.Molecules2023283132410.3390/molecules2803132436770990
     [Google Scholar]
  62. GagoF. Computational approaches to enzyme inhibition by marine natural products in the search for new drugs.Mar. Drugs202321210010.3390/md2102010036827141
     [Google Scholar]
  63. IsertC. AtzK. SchneiderG. Structure-based drug design with geometric deep learning.Curr. Opin. Struct. Biol.20237910254810.1016/j.sbi.2023.10254836842415
     [Google Scholar]
  64. ZhuK.F. YuanC. DuY.M. SunK.L. ZhangX.K. VogelH. JiaX.D. GaoY.Z. ZhangQ.F. WangD.P. ZhangH.W. Applications and prospects of cryo-EM in drug discovery.Mil. Med. Res.20231011010.1186/s40779‑023‑00446‑y36872349
     [Google Scholar]
  65. PavanM. MoroS. Lessons learnt from COVID-19: Computational strategies for facing present and future pandemics.Int. J. Mol. Sci.2023245440110.3390/ijms2405440136901832
     [Google Scholar]
  66. ZuoK. KranjcA. CapelliR. RossettiG. NechushtaiR. CarloniP. Metadynamics simulations of ligands binding to protein surfaces: A novel tool for rational drug design.Phys. Chem. Chem. Phys.20232520138191382410.1039/D3CP01388J37184538
     [Google Scholar]
  67. PangX. XuW. LiuY. LiH. ChenL. The research progress of SARS-CoV-2 main protease inhibitors from 2020 to 2022.Eur. J. Med. Chem.202325711549110.1016/j.ejmech.2023.11549137244162
     [Google Scholar]
  68. PliushcheuskayaP. KünzeG. Recent advances in computer-aided structure-based drug design on ion channels.Int. J. Mol. Sci.20232411922610.3390/ijms2411922637298178
     [Google Scholar]
  69. MateevE. GeorgievaM. MateevaA. ZlatkovA. AhmadS. RazaK. AzevedoV. BarhD. Structure-based design of novel MAO-B inhibitors: A review.Molecules20232812481410.3390/molecules2812481437375370
     [Google Scholar]
  70. HanR. YoonH. KimG. LeeH. LeeY. Revolutionizing medicinal chemistry: The application of artificial intelligence (AI) in early drug discovery.Pharmaceuticals2023169125910.3390/ph1609125937765069
     [Google Scholar]
  71. da Silva CalixtoP. de AlmeidaR.N. Stiebbe SalvadoriM.G.S. dos Santos MaiaM. FilhoJ.M.B. ScottiM.T. ScottiL. In silico study examining new phenylpropanoids targets with antidepressant activity.Curr. Drug Targets202122553955410.2174/138945012166620090217183832881667
     [Google Scholar]
  72. MinibaevaG. IvanovaA. PolishchukP. EasyDock: Customizable and scalable docking tool.J. Cheminform.202315110210.1186/s13321‑023‑00772‑237915072
     [Google Scholar]
  73. ZeyaullahM. KhanN. MuzammilK. AlShahraniA.M. KhanM.S. AlamM.S. AhmadR. KhanW.H. In-silico approaches for identification of compounds inhibiting SARS-CoV-2 3CL protease.PLoS One2023184e028430110.1371/journal.pone.028430137058496
     [Google Scholar]
  74. GhoshS. ChoS.J. Three-dimensional-QSAR and relative binding affinity estimation of focal adhesion kinase inhibitors.Molecules2023283146410.3390/molecules2803146436771129
     [Google Scholar]
  75. NgoS.T. NguyenT.H. TungN.T. VuV.V. PhamM.Q. MaiB.K. Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches.Phys. Chem. Chem. Phys.20222448292662927810.1039/D2CP04476E36449268
     [Google Scholar]
  76. NguyenT.H. TamN.M. TuanM.V. ZhanP. VuV.V. QuangD.T. NgoS.T. Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations.Chem. Phys.202356411170910.1016/j.chemphys.2022.11170936188488
     [Google Scholar]
  77. KrasoulisA. AntonopoulosN. PitsikalisV. TheodorakisS. DENVIS: Scalable and high-throughput virtual screening using graph neural networks with atomic and surface protein pocket features.J. Chem. Inf. Model.202262194642465910.1021/acs.jcim.2c0105736154119
     [Google Scholar]
  78. ReisP.B.P.S. BertoliniM. MontanariF. RocchiaW. MachuqueiroM. ClevertD.A. A fast and interpretable deep learning approach for accurate electrostatics-driven p K a predictions in proteins.J. Chem. Theory Comput.20221885068507810.1021/acs.jctc.2c0030835837736
     [Google Scholar]
  79. ClevesA.E. JohnsonS.R. JainA.N. Synergy and complementarity between focused machine learning and physics-based simulation in affinity prediction.J. Chem. Inf. Model.202161125948596610.1021/acs.jcim.1c0138234890185
     [Google Scholar]
  80. YangY. YaoK. RepaskyM.P. LeswingK. AbelR. ShoichetB.K. JeromeS.V. Efficient exploration of chemical space with docking and deep learning.J. Chem. Theory Comput.202117117106711910.1021/acs.jctc.1c0081034592101
     [Google Scholar]
  81. AlrasheedS. Principles of Mechanics. Fundamental University Physics. Advances in Science, Technology & Innovation.BerlinSpringer International Publishing201910.1007/978‑3‑030‑15195‑9
     [Google Scholar]
  82. MayoS.L. OlafsonB.D. GoddardW.A. DREIDING: A generic force field for molecular simulations.J. Phys. Chem.199094268897890910.1021/j100389a010
     [Google Scholar]
  83. KarimipourA. AminiA. NouriM. D’OrazioA. SabetvandR. HekmatifarM. MarjaniA. BachQ. Molecular dynamics performance for coronavirus simulation by C, N, O, and S atoms implementation dreiding force field: Drug delivery atomic interaction in contact with metallic Fe, Al, and steel.Comput. Part. Mech.20218473774910.1007/s40571‑020‑00367‑w33224712
     [Google Scholar]
  84. LimaK.A.L. Ribeiro JúniorL.A. Formation and stability of nanoscrolls composed of graphene and hexagonal boron nitride nanoribbons: Insights from molecular dynamics simulations.J. Mol. Model.2023291133910.1007/s00894‑023‑05702‑537837452
     [Google Scholar]
  85. HacisuleymanA. ErmanB. Fine tuning rigid body docking results using the dreiding force field: A computational study of 36 known nanobody-protein complexes.Proteins202391101417142610.1002/prot.2652937232507
     [Google Scholar]
  86. GuY. LiuM. StakerB.L. BuchkoG.W. QuinnR.J. Drug-repurposing screening identifies a gallic acid binding site on SARS-CoV-2 non-structural protein 7.ACS Pharmacol. Transl. Sci.20236457858610.1021/acsptsci.2c0022537082753
     [Google Scholar]
  87. Bitencourt-FerreiraG. de AzevedoW.F.Jr Machine learning to predict binding affinity.Methods Mol. Biol.2019205325127310.1007/978‑1‑4939‑9752‑7_1631452110
     [Google Scholar]
  88. DiakouI. PapakonstantinouE. PapageorgiouL. PierouliK. DragoumaniK. SpandidosD. BacopoulouF. ChrousosG. EliopoulosE. VlachakisD. Novel computational pipelines in antiviral structure-based drug design.Biomed. Rep.20221769710.3892/br.2022.158036382260
     [Google Scholar]
  89. MeliR. MorrisG.M. BigginP.C. Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: A review.Frontiers in Bioinformatics2022288598310.3389/fbinf.2022.88598336187180
     [Google Scholar]
  90. DeaneC. MokayaM. A virtual drug-screening approach to conquer huge chemical libraries.Nature2022601789332232310.1038/d41586‑021‑03682‑134912059
     [Google Scholar]
  91. JiménezJ. ŠkaličM. Martínez-RosellG. De FabritiisG. K DEEP : Protein–ligand absolute binding affinity prediction via 3D-convolutional neural networks.J. Chem. Inf. Model.201858228729610.1021/acs.jcim.7b0065029309725
     [Google Scholar]
  92. PiresD.E.V. AscherD.B. CSM-lig: A web server for assessing and comparing protein–small molecule affinities.Nucleic Acids Res.201644W1W557W56110.1093/nar/gkw39027151202
     [Google Scholar]
  93. WalshI. FishmanD. Garcia-GasullaD. TitmaT. PollastriG. CapriottiE. CasadioR. Capella-GutierrezS. CirilloD. Del ConteA. DimopoulosA.C. Del AngelV.D. DopazoJ. FariselliP. FernándezJ.M. HuberF. KreshukA. LenaertsT. MartelliP.L. NavarroA. BroinP.Ó. PiñeroJ. PiovesanD. ReczkoM. RonzanoF. SatagopamV. SavojardoC. SpiwokV. TangaroM.A. TartariG. SalgadoD. ValenciaA. ZambelliF. HarrowJ. PsomopoulosF.E. TosattoS.C.E. ELIXIR machine learning focus group. DOME: Recommendations for supervised machine learning validation in biology.Nat. Methods202118101122112710.1038/s41592‑021‑01205‑434316068
     [Google Scholar]
  94. Bitencourt-FerreiraG. RizzottoC. de Azevedo JuniorW.F. Machine learning-based scoring functions, development and applications with SAnDReS.Curr. Med. Chem.20212891746175610.2174/1875533XMTA25NjQu432410551
     [Google Scholar]
  95. Bitencourt-FerreiraG. de AzevedoW.F.Jr SAnDReS: A computational tool for docking.Methods Mol. Biol.20192053516510.1007/978‑1‑4939‑9752‑7_431452098
     [Google Scholar]
  96. Bitencourt-FerreiraG. de AzevedoW.F. Development of a machine-learning model to predict gibbs free energy of binding for protein-ligand complexes.Biophys. Chem.2018240636910.1016/j.bpc.2018.05.01029906639
     [Google Scholar]
  97. GramaticaP. On the development and validation of QSAR models.Methods Mol. Biol.201393049952610.1007/978‑1‑62703‑059‑5_2123086855
     [Google Scholar]
  98. Filgueira de AzevedoW.Jr CanduriF. Freitas da SilveiraN.J. Structural basis for inhibition of cyclin-dependent kinase 9 by flavopiridol.Biochem. Biophys. Res. Commun.2002293156657110.1016/S0006‑291X(02)00266‑812054639
     [Google Scholar]
  99. BarakatA. AlshahraniS. Al-MajidA.M. AlamaryA.S. HaukkaM. Abu-SerieM.M. DomingoL.R. AshrafS. Ul-HaqZ. NafieM.S. TelebM. New spiro-indeno[1,2- b ]quinoxalines clubbed with benzimidazole scaffold as CDK2 inhibitors for halting non-small cell lung cancer; stereoselective synthesis, molecular dynamics and structural insights.J. Enzyme Inhib. Med. Chem.2023381228126010.1080/14756366.2023.228126037994663
     [Google Scholar]
  100. Al-QadhiM.A. AllamH.A. FahimS.H. YahyaT.A.A. RagabF.A.F. Design and synthesis of certain 7-Aryl-2-Methyl-3-Substituted Pyrazolo1,5-aPyrimidines as multikinase inhibitors.Eur. J. Med. Chem.202326211591810.1016/j.ejmech.2023.11591837922829
     [Google Scholar]
  101. SalemM.E. MahrousE.M. RagabE.A. NafieM.S. DawoodK.M. Synthesis and anti-breast cancer potency of mono- and bis-(pyrazolyl[1,2,4]triazolo[3,4- b ][1,3,4]thiadiazine) derivatives as EGFR/CDK-2 target inhibitors.ACS Omega2023838353593536910.1021/acsomega.3c0530937779952
     [Google Scholar]
  102. ZengM. GrandnerJ.M. BryanM.C. VermaV. Larouche-GauthierR. LeclercJ.P. ZhaoL. HaghshenasP. Aubert-NicolS. YadavA. AshleyM. ChenJ.Z. DurkM. SamyK.E. NespiM. LevyE. MerrickK. MoffatJ.G. MurrayJ. OhA. OrrC. SegalE. SimsJ. SneeringerC. PrangleyM. VartanianS. MagnusonS. ParrB.T. Discovery of selective tertiary amide inhibitors of cyclin-dependent kinase 2 (CDK2).ACS Med. Chem. Lett.20231491179118710.1021/acsmedchemlett.3c0014237736184
     [Google Scholar]
  103. EltamanyE.E. NafieM.S. HalD.M. Abdel-KaderM.S. Abu-ElsaoudA.M. AhmedS.A. IbrahimA.K. BadrJ.M. AbdelhameedR.F.A. A new saponin (Zygo-albuside D) from Zygophyllum album roots triggers apoptosis in non-small cell lung carcinoma (A549 Cells) through CDK-2 inhibition.ACS Omega2023833306303063910.1021/acsomega.3c0431437636931
     [Google Scholar]
  104. AltharawiA. AlanaziM.M. AlossaimiM.A. AlanaziA.S. AlqahtaniS.M. GeesiM.H. RiadiY. Novel 2-Sulfanylquinazolin-4(3H)-one derivatives as multi-kinase inhibitors and apoptosis inducers: A synthesis, biological evaluation, and molecular docking study.Molecules20232814554810.3390/molecules2814554837513420
     [Google Scholar]
  105. De AzevedoW.F.Jr Mueller-DieckmannH.J. Schulze-GahmenU. WorlandP.J. SausvilleE. KimS.H. Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase.Proc. Natl. Acad. Sci. USA19969372735274010.1073/pnas.93.7.27358610110
     [Google Scholar]
  106. HernandesM. CavalcantiS.M. MoreiraD.R. de Azevedo JuniorW. LeiteA.C. Halogen atoms in the modern medicinal chemistry: Hints for the drug design.Curr. Drug Targets201011330331410.2174/13894501079071199620210755
     [Google Scholar]
  107. SuM. YangQ. DuY. FengG. LiuZ. LiY. WangR. Comparative assessment of scoring functions: The CASF-2016 update.J. Chem. Inf. Model.201959289591310.1021/acs.jcim.8b0054530481020
     [Google Scholar]
  108. Walter FA.J. Machine learning for drug science.Exploration of Drug Science202312778010.37349/eds.2023.00007
     [Google Scholar]
  109. CanduriF. SilvaR.G. dos SantosD.M. PalmaM.S. BassoL.A. SantosD.S. de AzevedoW.F.Jr Structure of human PNP complexed with ligands.Acta Crystallogr. D Biol. Crystallogr.200561785686210.1107/S090744490500542115983407
     [Google Scholar]
  110. YangC. ChenE.A. ZhangY. Protein–ligand docking in the machine-learning Era.Molecules20222714456810.3390/molecules2714456835889440
     [Google Scholar]
  111. JumperJ. EvansR. PritzelA. GreenT. FigurnovM. RonnebergerO. TunyasuvunakoolK. BatesR. ŽídekA. PotapenkoA. BridglandA. MeyerC. KohlS.A.A. BallardA.J. CowieA. Romera-ParedesB. NikolovS. JainR. AdlerJ. BackT. PetersenS. ReimanD. ClancyE. ZielinskiM. SteineggerM. PacholskaM. BerghammerT. BodensteinS. SilverD. VinyalsO. SeniorA.W. KavukcuogluK. KohliP. HassabisD. Highly accurate protein structure prediction with AlphaFold.Nature2021596787358358910.1038/s41586‑021‑03819‑234265844
     [Google Scholar]
/content/journals/cmc/10.2174/0109298673307315240730042209
Loading
Machine Learning Meets Physics-based Modeling: A Mass-spring System to Predict Protein-ligand Binding Affinity
Curr. Med. Chem. 32, 5882 (2025); https://doi.org/10.2174/0109298673307315240730042209
/content/journals/cmc/10.2174/0109298673307315240730042209
/content/journals/cmc/10.2174/0109298673307315240730042209
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Review Article
Keyword(s): artificial intelligence; CDK; deep learning; machine learning; mass-spring system; Physics-based model

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test