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2000
Volume 25, Issue 22
  • ISSN: 1568-0266
  • E-ISSN: 1873-4294

Abstract

The increasing importance of PROTACs (proteolysis-targeting chimeras) has attracted significant attention from both the academic community and industry. PROTACs are hetero-bifunctional small molecules that can bind to both the protein of interest (POI) and the E3 ubiquitin ligase (E3), inducing ubiquitinated degradation of POI. The unique mechanisms of PROTACs, such as event-driven pharmacology and modulation of protein degradation, provide novel therapeutic modalities for various diseases, including oncology, antiviral therapies, neurodegenerative diseases, acne, and others. Numerous computational methods, including structural prediction, molecular generation, and molecular dynamics simulation, have been applied in the development of PROTAC molecules. This review introduces the fundamental principles of computational tools used in PROTAC design, as well as typical examples validated by experiments.

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References

  1. SakamotoK.M. KimK.B. KumagaiA. MercurioF. CrewsC.M. DeshaiesR.J. PROTACs: Chimeric molecules that target proteins to the Skp1–Cullin–F box complex for ubiquitination and degradation.Proc. Natl. Acad. Sci. USA200198158554855910.1073/pnas.141230798 11438690
     [Google Scholar]
  2. GaddM.S. TestaA. LucasX. ChanK.H. ChenW. LamontD.J. ZengerleM. CiulliA. Structural basis of PROTAC cooperative recognition for selective protein degradation.Nat. Chem. Biol.201713551452110.1038/nchembio.2329 28288108
     [Google Scholar]
  3. QiS.M. DongJ. XuZ.Y. ChengX.D. ZhangW.D. QinJ.J. PROTAC: An effective targeted protein degradation strategy for cancer therapy.Front. Pharmacol.20211269257410.3389/fphar.2021.692574 34025443
     [Google Scholar]
  4. FinanC. GaultonA. KrugerF.A. LumbersR.T. ShahT. EngmannJ. GalverL. KelleyR. KarlssonA. SantosR. OveringtonJ.P. HingoraniA.D. CasasJ.P. The druggable genome and support for target identification and validation in drug development.Sci. Transl. Med.20179383eaag116610.1126/scitranslmed.aag1166 28356508
     [Google Scholar]
  5. FabbroD. 25 years of small molecular weight kinase inhibitors: Potentials and limitations.Mol. Pharmacol.201587576677510.1124/mol.114.095489 25549667
     [Google Scholar]
  6. AdjeiA.A. What is the right dose? The elusive optimal biologic dose in phase I clinical trials.J. Clin. Oncol.200624254054405510.1200/JCO.2006.07.4658 16943522
     [Google Scholar]
  7. KaramanM.W. HerrgardS. TreiberD.K. GallantP. AtteridgeC.E. CampbellB.T. ChanK.W. CiceriP. DavisM.I. EdeenP.T. FaraoniR. FloydM. HuntJ.P. LockhartD.J. MilanovZ.V. MorrisonM.J. PallaresG. PatelH.K. PritchardS. WodickaL.M. ZarrinkarP.P. A quantitative analysis of kinase inhibitor selectivity.Nat. Biotechnol.200826112713210.1038/nbt1358 18183025
     [Google Scholar]
  8. PosyS.L. HermsmeierM.A. VaccaroW. OttK.H. TodderudG. LippyJ.S. TrainorG.L. LoughneyD.A. JohnsonS.R. Trends in kinase selectivity: Insights for target class-focused library screening.J. Med. Chem.2011541546610.1021/jm101195a 21128601
     [Google Scholar]
  9. AbbasA. YeF. Computational methods and key considerations for in silico design of proteolysis targeting chimera (PROTACs).Int. J. Biol. Macromol.2024277Pt 413429310.1016/j.ijbiomac.2024.134293 39084437
     [Google Scholar]
  10. GeJ. HsiehC.Y. FangM. SunH. HouT. Development of PROTACs using computational approaches.Trends Pharmacol. Sci.202445121162117410.1016/j.tips.2024.10.006 39567313
     [Google Scholar]
  11. KriegerJ. SorrellF.J. WegenerA.A. LeuthnerB. Machrouhi-PorcherF. HechtM. LeibrockE.M. MüllerJ.E. EisertJ. HartungI.V. SchlesigerS. Systematic potency and property assessment of VHL Ligands and implications on PROTAC design.ChemMedChem2023188e20220061510.1002/cmdc.202200615 36749883
     [Google Scholar]
  12. KofinkC. TrainorN. MairB. WöhrleS. WurmM. MischerikowN. RoyM.J. BaderG. GrebP. GaravelG. DiersE. McLennanR. WhitworthC. VetmaV. RumpelK. ScharnweberM. FuchsJ.E. GerstbergerT. CuiY. GremelG. ChettaP. HopfS. BudanoN. RinnenthalJ. GmaschitzG. MayerM. KoeglM. CiulliA. WeinstablH. FarnabyW. A selective and orally bioavailable VHL-recruiting PROTAC achieves SMARCA2 degradation in vivo.Nat. Commun.2022131596910.1038/s41467‑022‑33430‑6 36216795
     [Google Scholar]
  13. FarnabyW. KoeglM. RoyM.J. WhitworthC. DiersE. TrainorN. ZollmanD. SteurerS. Karolyi-OezguerJ. RiedmuellerC. GmaschitzT. WachterJ. DankC. GalantM. SharpsB. RumpelK. TraxlerE. GerstbergerT. SchnitzerR. PetermannO. GrebP. WeinstablH. BaderG. ZoephelA. Weiss-PuxbaumA. Ehrenhöfer-WölferK. WöhrleS. BoehmeltG. RinnenthalJ. ArnhofH. WiechensN. WuM.Y. Owen-HughesT. EttmayerP. PearsonM. McConnellD.B. CiulliA. BAF complex vulnerabilities in cancer demonstrated via structure-based PROTAC design.Nat. Chem. Biol.201915767268010.1038/s41589‑019‑0294‑6 31178587
     [Google Scholar]
  14. ChungC. DaiH. FernandezE. TinworthC.P. ChurcherI. CryanJ. DenyerJ. HarlingJ.D. KonopackaA. QueisserM.A. TameC.J. WattG. JiangF. QianD. BenowitzA.B. Structural insights into PROTAC-mediated degradation of Bcl-xL.ACS Chem. Biol.20201592316232310.1021/acschembio.0c00266 32697072
     [Google Scholar]
  15. CyrusK. WehenkelM. ChoiE.Y. HanH.J. LeeH. SwansonH. KimK.B. Impact of linker length on the activity of PROTACs.Mol. Biosyst.20117235936410.1039/C0MB00074D 20922213
     [Google Scholar]
  16. WeerakoonD. CarbajoR.J. De MariaL. TyrchanC. ZhaoH. Impact of PROTAC linker plasticity on the solution conformations and dissociation of the ternary complex.J. Chem. Inf. Model.202262234034910.1021/acs.jcim.1c01036 35018781
     [Google Scholar]
  17. SmithB.E. WangS.L. Jaime-FigueroaS. HarbinA. WangJ. HammanB.D. CrewsC.M. Differential PROTAC substrate specificity dictated by orientation of recruited E3 ligase.Nat. Commun.201910113110.1038/s41467‑018‑08027‑7 30631068
     [Google Scholar]
  18. NowakR.P. DeAngeloS.L. BuckleyD. HeZ. DonovanK.A. AnJ. SafaeeN. JedrychowskiM.P. PonthierC.M. IshoeyM. ZhangT. ManciasJ.D. GrayN.S. BradnerJ.E. FischerE.S. Plasticity in binding confers selectivity in ligand-induced protein degradation.Nat. Chem. Biol.201814770671410.1038/s41589‑018‑0055‑y 29892083
     [Google Scholar]
  19. BondesonD.P. SmithB.E. BurslemG.M. BuhimschiA.D. HinesJ. Jaime-FigueroaS. WangJ. HammanB.D. IshchenkoA. CrewsC.M. Lessons in PROTAC design from selective degradation with a promiscuous warhead.Cell Chem. Biol.20182517887.e510.1016/j.chembiol.2017.09.010 29129718
     [Google Scholar]
  20. WengG. LiD. KangY. HouT. Integrative modeling of PROTAC-mediated ternary complexes.J. Med. Chem.20216421162711628110.1021/acs.jmedchem.1c01576 34709816
     [Google Scholar]
  21. KitchenD.B. DecornezH. FurrJ.R. BajorathJ. Docking and scoring in virtual screening for drug discovery: Methods and applications.Nat. Rev. Drug Discov.200431193594910.1038/nrd1549 15520816
     [Google Scholar]
  22. StanzioneF. GiangrecoI. ColeJ.C. Use of molecular docking computational tools in drug discovery.Prog. Med. Chem.20216027334310.1016/bs.pmch.2021.01.004 34147204
     [Google Scholar]
  23. MostofianB. MartinH.J. RazaviA. PatelS. AllenB. ShermanW. IzaguirreJ.A. Targeted protein degradation: Advances, challenges, and prospects for computational methods.J. Chem. Inf. Model.202363175408543210.1021/acs.jcim.3c00603 37602861
     [Google Scholar]
  24. DrummondM.L. WilliamsC.I. In silico modeling of PROTAC-mediated ternary complexes: Validation and application.J. Chem. Inf. Model.20195941634164410.1021/acs.jcim.8b00872 30714732
     [Google Scholar]
  25. ZaidmanD. PriluskyJ. LondonN. PRosettaC: Rosetta based modeling of PROTAC mediated ternary complexes.J. Chem. Inf. Model.202060104894490310.1021/acs.jcim.0c00589 32976709
     [Google Scholar]
  26. Schneidman-DuhovnyD InbarY NussinovR WolfsonHJ PatchDock and SymmDock: Servers for rigid and symmetric docking.Nucleic Acids Res200533Web Server issueW363W36710.1093/nar/gki481
     [Google Scholar]
  27. BaiN. MillerS.A. AndrianovG.V. YatesM. KirubakaranP. KaranicolasJ. Rationalizing PROTAC-mediated ternary complex formation using rosetta.J. Chem. Inf. Model.20216131368138210.1021/acs.jcim.0c01451 33625214
     [Google Scholar]
  28. DrummondM.L. HenryA. LiH. WilliamsC.I. Improved accuracy for modeling PROTAC-mediated ternary complex formation and targeted protein degradation via new in silico methodologies.J. Chem. Inf. Model.202060105234525410.1021/acs.jcim.0c00897 32969649
     [Google Scholar]
  29. IgnatovM. JindalA. KotelnikovS. BeglovD. PosternakG. TangX. MaisonneuveP. PodaG. BateyR.A. SicheriF. WhittyA. TongeP.J. VajdaS. KozakovD. High accuracy prediction of PROTAC complex structures.J. Am. Chem. Soc.2023145137123713510.1021/jacs.2c09387 36961978
     [Google Scholar]
  30. KozakovD. BrenkeR. ComeauS.R. VajdaS. PIPER: An FFT‐based protein docking program with pairwise potentials.Proteins200665239240610.1002/prot.21117 16933295
     [Google Scholar]
  31. DominguezC. BoelensR. BonvinA.M.J.J. HADDOCK: A protein-protein docking approach based on biochemical or biophysical information.J. Am. Chem. Soc.200312571731173710.1021/ja026939x 12580598
     [Google Scholar]
  32. KongR. WangF. ZhangJ. WangF. ChangS. CoDockPP: A multistage approach for global and site-specific protein–protein docking.J. Chem. Inf. Model.20195983556356410.1021/acs.jcim.9b00445 31276391
     [Google Scholar]
  33. PierceB.G. WieheK. HwangH. KimB.H. VrevenT. WengZ. ZDOCK server: Interactive docking prediction of protein–protein complexes and symmetric multimers.Bioinformatics201430121771177310.1093/bioinformatics/btu097 24532726
     [Google Scholar]
  34. KozakovD. HallD.R. XiaB. PorterK.A. PadhornyD. YuehC. BeglovD. VajdaS. The ClusPro web server for protein–protein docking.Nat. Protoc.201712225527810.1038/nprot.2016.169 28079879
     [Google Scholar]
  35. YanY. ZhangD. ZhouP. LiB. HuangS.Y. HDOCK: A web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy.Nucleic Acids Res.201745W1W365W37310.1093/nar/gkx407 28521030
     [Google Scholar]
  36. Jiménez-GarcíaB. Roel-TourisJ. Romero-DuranaM. VidalM. Jiménez-GonzálezD. Fernández-RecioJ. LightDock: A new multi-scale approach to protein–protein docking.Bioinformatics2018341495510.1093/bioinformatics/btx555 28968719
     [Google Scholar]
  37. OhueM. ShimodaT. SuzukiS. MatsuzakiY. IshidaT. AkiyamaY. MEGADOCK 4.0: An ultra–high-performance protein–protein docking software for heterogeneous supercomputers.Bioinformatics201430223281328310.1093/bioinformatics/btu532 25100686
     [Google Scholar]
  38. GarzonJ.I. Lopéz-BlancoJ.R. PonsC. KovacsJ. AbagyanR. Fernandez-RecioJ. ChaconP. FRODOCK: A new approach for fast rotational protein–protein docking.Bioinformatics200925192544255110.1093/bioinformatics/btp447 19620099
     [Google Scholar]
  39. IncC.C.G. Molecular operating environment (MOE).Montreal (Canada)Chemical Computing Group Inc20161010
     [Google Scholar]
  40. LiF. HuQ. ZhangX. SunR. LiuZ. WuS. TianS. MaX. DaiZ. YangX. GaoS. BaiF. DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs.Nat. Commun.2022131713310.1038/s41467‑022‑34807‑3 36414666
     [Google Scholar]
  41. ZhengS. TanY. WangZ. LiC. ZhangZ. SangX. ChenH. YangY. Accelerated rational PROTAC design via deep learning and molecular simulations.Nat. Mach. Intell.20224973974810.1038/s42256‑022‑00527‑y
     [Google Scholar]
  42. MslatiH. GentileF. PandeyM. BanF. CherkasovA. PROTACable is an integrative computational pipeline of 3-d modeling and deep learning to automate the de novo design of PROTACs.J. Chem. Inf. Model.20246483034304610.1021/acs.jcim.3c01878 38504115
     [Google Scholar]
  43. KaoC.T. LinC.T. ChouC.L. LinC.C. Fragment linker prediction using the deep encoder-decoder network for PROTACs drug design.J. Chem. Inf. Model.202363102918292710.1021/acs.jcim.2c01287 37150933
     [Google Scholar]
  44. VemulaD. JayasuryaP. SushmithaV. KumarY.N. BhandariV. CADD, AI and ML in drug discovery: A comprehensive review.Eur. J. Pharm. Sci.202318110632410.1016/j.ejps.2022.106324 36347444
     [Google Scholar]
  45. PaulD. SanapG. ShenoyS. KalyaneD. KaliaK. TekadeR.K. Artificial intelligence in drug discovery and development.Drug Discov. Today2021261809310.1016/j.drudis.2020.10.010 33099022
     [Google Scholar]
  46. SarkarC. DasB. RawatV.S. WahlangJ.B. NongpiurA. TiewsohI. LyngdohN.M. DasD. BidarolliM. SonyH.T. Artificial intelligence and machine learning technology driven modern drug discovery and development.Int. J. Mol. Sci.2023243202610.3390/ijms24032026 36768346
     [Google Scholar]
  47. KhanS.R. Al RijjalD. PiroA. WheelerM.B. Integration of AI and traditional medicine in drug discovery.Drug Discov. Today202126498299210.1016/j.drudis.2021.01.008 33476566
     [Google Scholar]
  48. GawehnE. HissJ.A. BrownJ.B. SchneiderG. Advancing drug discovery via GPU-based deep learning.Expert Opin. Drug Discov.201813757958210.1080/17460441.2018.1465407 29668343
     [Google Scholar]
  49. VamathevanJ. ClarkD. CzodrowskiP. DunhamI. FerranE. LeeG. LiB. MadabhushiA. ShahP. SpitzerM. ZhaoS. Applications of machine learning in drug discovery and development.Nat. Rev. Drug Discov.201918646347710.1038/s41573‑019‑0024‑5 30976107
     [Google Scholar]
  50. PatelL. ShuklaT. HuangX. UsseryD.W. WangS. Machine learning methods in drug discovery.Molecules20202522527710.3390/molecules25225277 33198233
     [Google Scholar]
  51. BourquinJ. SchmidliH. van HoogevestP. LeuenbergerH. Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development.Pharm. Dev. Technol.1997229510910.3109/10837459709022615 9552436
     [Google Scholar]
  52. Carracedo-ReboredoP. Liñares-BlancoJ. Rodríguez-FernándezN. CedrónF. NovoaF.J. CarballalA. MaojoV. PazosA. Fernandez-LozanoC. A review on machine learning approaches and trends in drug discovery.Comput. Struct. Biotechnol. J.2021194538455810.1016/j.csbj.2021.08.011 34471498
     [Google Scholar]
  53. WengG. ShenC. CaoD. GaoJ. DongX. HeQ. YangB. LiD. WuJ. HouT. PROTAC-DB: An online database of PROTACs.Nucleic Acids Res.202149D1D1381D138710.1093/nar/gkaa807 33010159
     [Google Scholar]
  54. WengG. CaiX. CaoD. DuH. ShenC. DengY. HeQ. YangB. LiD. HouT. PROTAC-DB 2.0: An updated database of PROTACs.Nucleic Acids Res.202351D1D1367D137210.1093/nar/gkac946 36300631
     [Google Scholar]
  55. WuX. XuL.Y. LiE.M. DongG. Application of molecular dynamics simulation in biomedicine.Chem. Biol. Drug Des.202299578980010.1111/cbdd.14038 35293126
     [Google Scholar]
  56. CollierT.A. PiggotT.J. AllisonJ.R. Molecular Dynamics Simulation of Proteins.Methods Mol. Biol.2020207331132710.1007/978‑1‑4939‑9869‑2_17 31612449
     [Google Scholar]
  57. FilipeH.A.L. LouraL.M.S. Molecular dynamics simulations: Advances and applications.Molecules2022277210510.3390/molecules27072105 35408504
     [Google Scholar]
  58. GuoW.H. QiX. YuX. LiuY. ChungC.I. BaiF. LinX. LuD. WangL. ChenJ. SuL.H. NomieK.J. LiF. WangM.C. ShuX. OnuchicJ.N. WoyachJ.A. WangM.L. WangJ. Enhancing intracellular accumulation and target engagement of PROTACs with reversible covalent chemistry.Nat. Commun.2020111426810.1038/s41467‑020‑17997‑6 32848159
     [Google Scholar]
  59. TestaA. HughesS.J. LucasX. WrightJ.E. CiulliA. Structure‐based design of a macrocyclic PROTAC.Angew. Chem. Int. Ed.20205941727173410.1002/anie.201914396 31746102
     [Google Scholar]
  60. ShaheerM. SinghR. SobhiaM.E. Protein degradation: A novel computational approach to design protein degrader probes for main protease of SARS-CoV-2.J. Biomol. Struct. Dyn.20224021109051091710.1080/07391102.2021.1953601 34328382
     [Google Scholar]
  61. ZorbaA. NguyenC. XuY. StarrJ. BorzilleriK. SmithJ. ZhuH. FarleyK.A. DingW. SchiemerJ. FengX. ChangJ.S. UccelloD.P. YoungJ.A. Garcia-IrrizaryC.N. CzabaniukL. SchuffB. OliverR. MontgomeryJ. HaywardM.M. CoeJ. ChenJ. NiosiM. LuthraS. ShahJ.C. El-KattanA. QiuX. WestG.M. NoeM.C. ShanmugasundaramV. GilbertA.M. BrownM.F. CalabreseM.F. Delineating the role of cooperativity in the design of potent PROTACs for BTK.Proc. Natl. Acad. Sci. USA201811531E7285E729210.1073/pnas.1803662115 30012605
     [Google Scholar]
  62. YangZ. SunY. NiZ. YangC. TongY. LiuY. LiH. RaoY. Merging PROTAC and molecular glue for degrading BTK and GSPT1 proteins concurrently.Cell Res.202131121315131810.1038/s41422‑021‑00533‑6 34417569
     [Google Scholar]
  63. MurakamiY. OsawaH. KuroharaT. YanaseY. ItoT. YokooH. ShibataN. NaitoM. AritakeK. DemizuY. Structure–activity relationship study of PROTACs against hematopoietic prostaglandin D 2 synthase.RSC Medicinal. Chemistry.202213121495150310.1039/D2MD00284A 36561070
     [Google Scholar]
  64. LucasX. Van MolleI. CiulliA. Surface probing by fragment-based screening and computational methods identifies ligandable pockets on the von hippel–lindau (VHL) E3 ubiquitin ligase.J. Med. Chem.201861167387739310.1021/acs.jmedchem.8b00842 30040896
     [Google Scholar]
  65. XueX. KangJ.B. YangX. LiN. ChangL. JiJ. MengX.K. ZhangH.Q. ZhongY. YuS.P. WuW.Y. WangX.L. LiN.G. SunS.L. An efficient strategy for digging protein-protein interactions for rational drug design - A case study with HIF-1α/VHL.Eur. J. Med. Chem.202222711387110.1016/j.ejmech.2021.113871 34638033
     [Google Scholar]
  66. CarlsonH.A. Protein flexibility and drug design: How to hit a moving target.Curr. Opin. Chem. Biol.20026444745210.1016/S1367‑5931(02)00341‑1 12133719
     [Google Scholar]
  67. CozziniP. KelloggG.E. SpyrakisF. AbrahamD.J. CostantinoG. EmersonA. FanelliF. GohlkeH. KuhnL.A. MorrisG.M. OrozcoM. PertinhezT.A. RizziM. SotrifferC.A. Target flexibility: An emerging consideration in drug discovery and design.J. Med. Chem.200851206237625510.1021/jm800562d 18785728
     [Google Scholar]
  68. LiaoJ. NieX. UnartaI.C. EricksenS.S. TangW. In silico modeling and scoring of PROTAC-mediated ternary complex poses.J. Med. Chem.20226586116613210.1021/acs.jmedchem.1c02155 35412837
     [Google Scholar]
  69. DixonT. MacPhersonD. MostofianB. DauzhenkaT. LotzS. McGeeD. ShechterS. ShresthaU.R. WiewioraR. McDarghZ.A. PeiF. PalR. RibeiroJ.V. WilkersonT. SachdevaV. GaoN. JainS. SparksS. LiY. VinitskyA. ZhangX. RazaviA.M. KolossváryI. ImbriglioJ. EvdokimovA. BergeronL. ZhouW. AdhikariJ. RuprechtB. DicksonA. XuH. ShermanW. IzaguirreJ.A. Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry.Nat. Commun.2022131588410.1038/s41467‑022‑33575‑4 36202813
     [Google Scholar]
  70. LiW. ZhangJ. GuoL. WangQ. Importance of three-body problems and protein–protein interactions in proteolysis-targeting chimera modeling: Insights from molecular dynamics simulations.J. Chem. Inf. Model.202262352353210.1021/acs.jcim.1c01150 35084845
     [Google Scholar]
  71. HusicB.E. PandeV.S. Markov state models: From an art to a science.J. Am. Chem. Soc.201814072386239610.1021/jacs.7b12191 29323881
     [Google Scholar]
  72. PandeV.S. BeauchampK. BowmanG.R. Everything you wanted to know about Markov State Models but were afraid to ask.Methods20105219910510.1016/j.ymeth.2010.06.002 20570730
     [Google Scholar]
  73. SidkyH. ChenW. FergusonA.L. Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation.Mol. Phys.20201185e173774210.1080/00268976.2020.1737742
     [Google Scholar]
  74. JonesM.S. McDarghZ.A. WiewioraR.P. IzaguirreJ.A. XuH. FergusonA.L. Molecular latent space simulators for distributed and multimolecular trajectories.J. Phys. Chem. A2023127255470549010.1021/acs.jpca.3c01362 37314375
     [Google Scholar]
  75. PavanM. MeninS. BassaniD. SturleseM. MoroS. Qualitative estimation of protein–ligand complex stability through thermal titration molecular dynamics simulations.J. Chem. Inf. Model.202262225715572810.1021/acs.jcim.2c00995 36315402
     [Google Scholar]
  76. A S, B.G.; Agrawal, D.; Kulkarni, N.M.; Vetrivel, R.; Gurram, K. PROTAC-design-evaluator (PRODE): An advanced method for in-silico PROTAC design.ACS Omega2024911acsomega.3c0731810.1021/acsomega.3c0731838524483
     [Google Scholar]
  77. JingB. CorsoG. ChangJ. BarzilayR. JaakkolaT. Torsional diffusion for molecular conformer generation.Adv. Neural Inf. Process. Syst.2022352424024253
     [Google Scholar]
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The Applications of Computational Tools in PROTAC Development
Curr. Top. Med. Chem. 25, 2573 (2025); https://doi.org/10.2174/0115680266365625250313061523
/content/journals/ctmc/10.2174/0115680266365625250313061523
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  • Article Type:     Review Article
Keyword(s): artificial intelligence; molecular docking; molecular dynamics simulation; PROTAC; protein degradation

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