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2000
Volume 26, Issue 2
  • ISSN: 1389-2002
  • E-ISSN: 1875-5453
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2025-06-18
2026-01-20
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References

  1. Sree KommalapatiH. PilliP. GollaV.M. BhattN. SamanthulaG. In silico tools to thaw the complexity of the data: Revolutionizing drug research in drug metabolism, pharmacokinetics and toxicity prediction.Curr. Drug Metab.2023241173575510.2174/0113892002270798231201111422 38058088
    [Google Scholar]
  2. YinJ. LiF. ZhouY. MouM. LuY. ChenK. XueJ. LuoY. FuJ. HeX. GaoJ. ZengS. YuL. ZhuF. INTEDE: interactome of drug-metabolizing enzymes.Nucleic Acids Res.202149D1D1233D124310.1093/nar/gkaa755 33045737
    [Google Scholar]
  3. DudasB. MitevaM.A. Computational and artificial intelligence-based approaches for drug metabolism and transport prediction.Trends Pharmacol. Sci.2024451395510.1016/j.tips.2023.11.001 38072723
    [Google Scholar]
  4. TranT.T.V. TayaraH. ChongK.T. Artificial intelligence in drug metabolism and excretion prediction: recent advances, challenges, and future perspectives.Pharmaceutics2023154126010.3390/pharmaceutics15041260 37111744
    [Google Scholar]
  5. YehA.H.W. NornC. KipnisY. TischerD. PellockS.J. EvansD. MaP. LeeG.R. ZhangJ.Z. AnishchenkoI. CoventryB. CaoL. DauparasJ. HalabiyaS. DeWittM. CarterL. HoukK.N. BakerD. De novo design of luciferases using deep learning.Nature2023614794977478010.1038/s41586‑023‑05696‑3 36813896
    [Google Scholar]
  6. WangQ. LiuX. ZhangH. ChuH. ShiC. ZhangL. Cytochrome P450 enzyme design by constraining the catalytic pocket in a diffusion model.Research (Wash D C)202487041310.34133/research.0413
    [Google Scholar]
  7. DanelT. WojtuchA. PodlewskaS. Generation of new inhibitors of selected cytochrome P450 subtypes– In silico study.Comput. Struct. Biotechnol. J.2022205639565110.1016/j.csbj.2022.10.005 36284709
    [Google Scholar]
  8. LuoY.B. HouY.Y. WangZ. HuX.M. LiW. LiY. LiuY. LiT.J. AiC.Z. Computational prediction for the metabolism of human UDP-glucuronosyltransferase 1A1 substrates.Comput. Biol. Med.202214910595910.1016/j.compbiomed.2022.105959 36063691
    [Google Scholar]
  9. FuL. ShiS. YiJ. WangN. HeY. WuZ. PengJ. DengY. WangW. WuC. LyuA. ZengX. ZhaoW. HouT. CaoD. ADMETlab 3.0: An updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support.Nucleic Acids Res.202452W1W422W43110.1093/nar/gkae236 38572755
    [Google Scholar]
  10. TianS. CaoX. GreinerR. LiC. GuoA. WishartD.S. CyProduct: A software tool for accurately predicting the byproducts of human Cytochrome P450 metabolism.J. Chem. Inf Model20216163128314010.1021/acs.jcim.1c00144 34038112
    [Google Scholar]
  11. de Bruyn KopsC. ŠíchoM. MazzolariA. KirchmairJ. GLORYx: prediction of the metabolites resulting from phase 1 and phase 2 biotransformations of xenobiotics.Chem. Res. Toxicol.202134228629910.1021/acs.chemrestox.0c00224 32786543
    [Google Scholar]
  12. YinJ. YouN. LiF. LuM. ZengS. ZhuF. State-of-the-art application of artificial intelligence to transporter-centered functional and pharmaceutical research.Curr. Drug Metab.202324316217410.2174/1389200224666230523155759 37226790
    [Google Scholar]
  13. YinJ. ChenZ. YouN. LiF. ZhangH. XueJ. MaH. ZhaoQ. YuL. ZengS. ZhuF. VARIDT 3.0: The phenotypic and regulatory variability of drug transporter.Nucleic Acids Res.202452D1D1490D150210.1093/nar/gkad818 37819041
    [Google Scholar]
  14. AbramsonJ. AdlerJ. DungerJ. EvansR. GreenT. PritzelA. RonnebergerO. WillmoreL. BallardA.J. BambrickJ. BodensteinS.W. EvansD.A. HungC.C. O’NeillM. ReimanD. TunyasuvunakoolK. WuZ. ŽemgulytėA. ArvanitiE. BeattieC. BertolliO. BridglandA. CherepanovA. CongreveM. Cowen-RiversA.I. CowieA. FigurnovM. FuchsF.B. GladmanH. JainR. KhanY.A. LowC.M.R. PerlinK. PotapenkoA. SavyP. SinghS. SteculaA. ThillaisundaramA. TongC. YakneenS. ZhongE.D. ZielinskiM. ŽídekA. BapstV. KohliP. JaderbergM. HassabisD. JumperJ.M. Accurate structure prediction of biomolecular interactions with AlphaFold 3.Nature2024630801649350010.1038/s41586‑024‑07487‑w 38718835
    [Google Scholar]
  15. MyungY. de SáA.G.C. AscherD.B. Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction.Nucleic Acids Res.202452W1W469W47510.1093/nar/gkae254 38634808
    [Google Scholar]
  16. ObrezanovaO. Artificial intelligence for compound pharmacokinetics prediction.Curr. Opin. Struct. Biol.20237910254610.1016/j.sbi.2023.102546 36804676
    [Google Scholar]
  17. ChouW.C. LinZ. Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.Toxicol. Sci.2023191111410.1093/toxsci/kfac101 36156156
    [Google Scholar]
  18. WuK. LiX. ZhouZ. ZhaoY. SuM. ChengZ. WuX. HuangZ. JinX. LiJ. ZhangM. LiuJ. LiuB. Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling.Front. Pharmacol.202415133085510.3389/fphar.2024.1330855 38434709
    [Google Scholar]
  19. ZhangK. YangX. WangY. YuY. HuangN. LiG. LiX. WuJ.C. YangS. Artificial intelligence in drug development.Nat. Med.2025311455910.1038/s41591‑024‑03434‑4 39833407
    [Google Scholar]
  20. ZhouY. ZhangY. ZhangZ. ZhouZ. ZhuF. AI comes to the Nobel Prize and drug discovery.J. Pharm. Anal.2024141110116010.1016/j.jpha.2024.101160 39850961
    [Google Scholar]
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