BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

352 related articles for article (PubMed ID: 29775322)

  • 1. Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network.
    Li X; Xu Y; Lai L; Pei J
    Mol Pharm; 2018 Oct; 15(10):4336-4345. PubMed ID: 29775322
    [TBL] [Abstract][Full Text] [Related]  

  • 2. ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches.
    Wu Z; Lei T; Shen C; Wang Z; Cao D; Hou T
    J Chem Inf Model; 2019 Nov; 59(11):4587-4601. PubMed ID: 31644282
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers.
    Cheng F; Yu Y; Shen J; Yang L; Li W; Liu G; Lee PW; Tang Y
    J Chem Inf Model; 2011 May; 51(5):996-1011. PubMed ID: 21491913
    [TBL] [Abstract][Full Text] [Related]  

  • 4. iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding.
    Nguyen-Vo TH; Trinh QH; Nguyen L; Nguyen-Hoang PU; Nguyen TN; Nguyen DT; Nguyen BP; Le L
    J Chem Inf Model; 2022 Nov; 62(21):5059-5068. PubMed ID: 34672553
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Insights into molecular basis of cytochrome p450 inhibitory promiscuity of compounds.
    Cheng F; Yu Y; Zhou Y; Shen Z; Xiao W; Liu G; Li W; Lee PW; Tang Y
    J Chem Inf Model; 2011 Oct; 51(10):2482-95. PubMed ID: 21875141
    [TBL] [Abstract][Full Text] [Related]  

  • 6. In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method.
    Lee JH; Basith S; Cui M; Kim B; Choi S
    SAR QSAR Environ Res; 2017 Oct; 28(10):863-874. PubMed ID: 29183231
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.
    Xu Y; Ma J; Liaw A; Sheridan RP; Svetnik V
    J Chem Inf Model; 2017 Oct; 57(10):2490-2504. PubMed ID: 28872869
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition.
    Chen Z; Zhang L; Zhang P; Guo H; Zhang R; Li L; Li X
    J Chem Inf Model; 2024 Apr; 64(7):2528-2538. PubMed ID: 37864562
    [TBL] [Abstract][Full Text] [Related]  

  • 9. CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes.
    Plonka W; Stork C; Šícho M; Kirchmair J
    Bioorg Med Chem; 2021 Sep; 46():116388. PubMed ID: 34488021
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A unified GCNN model for predicting CYP450 inhibitors by using graph convolutional neural networks with attention mechanism.
    Qiu M; Liang X; Deng S; Li Y; Ke Y; Wang P; Mei H
    Comput Biol Med; 2022 Nov; 150():106177. PubMed ID: 36242811
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction.
    Xu Y; Pei J; Lai L
    J Chem Inf Model; 2017 Nov; 57(11):2672-2685. PubMed ID: 29019671
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Computational prediction of cytochrome P450 inhibition and induction.
    Kato H
    Drug Metab Pharmacokinet; 2020 Feb; 35(1):30-44. PubMed ID: 31902468
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Computational Prediction of Inhibitors and Inducers of the Major Isoforms of Cytochrome P450.
    Rudik A; Dmitriev A; Lagunin A; Filimonov D; Poroikov V
    Molecules; 2022 Sep; 27(18):. PubMed ID: 36144612
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Flavokawain A inhibits Cytochrome P450 in in vitro metabolic and inhibitory investigations.
    Niu L; Ding L; Lu C; Zuo F; Yao K; Xu S; Li W; Yang D; Xu X
    J Ethnopharmacol; 2016 Sep; 191():350-359. PubMed ID: 27318274
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets.
    Wenzel J; Matter H; Schmidt F
    J Chem Inf Model; 2019 Mar; 59(3):1253-1268. PubMed ID: 30615828
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule.
    Mishra NK; Agarwal S; Raghava GP
    BMC Pharmacol; 2010 Jul; 10():8. PubMed ID: 20637097
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models.
    Gleeson MP; Davis AM; Chohan KK; Paine SW; Boyer S; Gavaghan CL; Arnby CH; Kankkonen C; Albertson N
    J Comput Aided Mol Des; 2007; 21(10-11):559-73. PubMed ID: 18034311
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing.
    Ouzounis S; Panagiotopoulos V; Bafiti V; Zoumpoulakis P; Cavouras D; Kalatzis I; Matsoukas MT; Katsila T
    OMICS; 2023 Jul; 27(7):305-314. PubMed ID: 37406257
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data.
    Sun H; Veith H; Xia M; Austin CP; Huang R
    J Chem Inf Model; 2011 Oct; 51(10):2474-81. PubMed ID: 21905670
    [TBL] [Abstract][Full Text] [Related]  

  • 20. [Computational Pharmacological Study on Clopidogrel Metabolism Enzymes Influenced by Fufang Danshen Dripping Pill].
    Ma ST; Dai GL; Bi XL; Gong MR; Sun BT; Ju WZ; Tan HS
    Zhong Yao Cai; 2015 May; 38(5):1009-12. PubMed ID: 26767297
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 18.