BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

303 related articles for article (PubMed ID: 30110511)

  • 1. In Silico Prediction of Blood-Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods.
    Wang Z; Yang H; Wu Z; Wang T; Li W; Tang Y; Liu G
    ChemMedChem; 2018 Oct; 13(20):2189-2201. PubMed ID: 30110511
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods.
    Liu L; Zhang L; Feng H; Li S; Liu M; Zhao J; Liu H
    Chem Res Toxicol; 2021 Jun; 34(6):1456-1467. PubMed ID: 34047182
    [TBL] [Abstract][Full Text] [Related]  

  • 3. In silico modeling on ADME properties of natural products: Classification models for blood-brain barrier permeability, its application to traditional Chinese medicine and in vitro experimental validation.
    Zhang X; Liu T; Fan X; Ai N
    J Mol Graph Model; 2017 Aug; 75():347-354. PubMed ID: 28628860
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.
    Yuan Y; Zheng F; Zhan CG
    AAPS J; 2018 Mar; 20(3):54. PubMed ID: 29564576
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood-brain Barrier Entry of Chemical Compounds Using Machine Learning.
    Majumdar S; Basak SC; Lungu CN; Diudea MV; Grunwald GD
    Mol Inform; 2019 Aug; 38(8-9):e1800164. PubMed ID: 31322827
    [TBL] [Abstract][Full Text] [Related]  

  • 6. QSAR modeling of the blood-brain barrier permeability for diverse organic compounds.
    Zhang L; Zhu H; Oprea TI; Golbraikh A; Tropsha A
    Pharm Res; 2008 Aug; 25(8):1902-14. PubMed ID: 18553217
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Machine learning based dynamic consensus model for predicting blood-brain barrier permeability.
    Mazumdar B; Deva Sarma PK; Mahanta HJ; Sastry GN
    Comput Biol Med; 2023 Jun; 160():106984. PubMed ID: 37137267
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A method to predict different mechanisms for blood-brain barrier permeability of CNS activity compounds in Chinese herbs using support vector machine.
    Jiang L; Chen J; He Y; Zhang Y; Li G
    J Bioinform Comput Biol; 2016 Feb; 14(1):1650005. PubMed ID: 26632324
    [TBL] [Abstract][Full Text] [Related]  

  • 9. [Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds].
    Su Q; Xiao G; Zhou W; Du Z
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2023 Aug; 40(4):753-761. PubMed ID: 37666766
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A Recurrent Neural Network model to predict blood-brain barrier permeability.
    Alsenan S; Al-Turaiki I; Hafez A
    Comput Biol Chem; 2020 Dec; 89():107377. PubMed ID: 33010784
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Blood Brain Barrier Permeability Prediction Using Machine Learning Techniques: An Update.
    Saxena D; Sharma A; Siddiqui MH; Kumar R
    Curr Pharm Biotechnol; 2019; 20(14):1163-1171. PubMed ID: 31433750
    [TBL] [Abstract][Full Text] [Related]  

  • 12. ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.
    Lei T; Sun H; Kang Y; Zhu F; Liu H; Zhou W; Wang Z; Li D; Li Y; Hou T
    Mol Pharm; 2017 Nov; 14(11):3935-3953. PubMed ID: 29037046
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling.
    Pham-The H; Casañola-Martin G; Garrigues T; Bermejo M; González-Álvarez I; Nguyen-Hai N; Cabrera-Pérez MÁ; Le-Thi-Thu H
    Mol Divers; 2016 Feb; 20(1):93-109. PubMed ID: 26643659
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of blood-brain barrier permeability of organic compounds.
    Dyabina AS; Radchenko EV; Palyulin VA; Zefirov NS
    Dokl Biochem Biophys; 2016 Sep; 470(1):371-374. PubMed ID: 27817020
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds.
    Radchenko EV; Dyabina AS; Palyulin VA
    Molecules; 2020 Dec; 25(24):. PubMed ID: 33322142
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Qualitative and quantitative structure-activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals.
    Gupta S; Basant N; Singh KP
    SAR QSAR Environ Res; 2015; 26(2):95-124. PubMed ID: 25629764
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Integrating in Silico and in Vitro Approaches To Predict Drug Accessibility to the Central Nervous System.
    Zhang YY; Liu H; Summerfield SG; Luscombe CN; Sahi J
    Mol Pharm; 2016 May; 13(5):1540-50. PubMed ID: 27015243
    [TBL] [Abstract][Full Text] [Related]  

  • 18. DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy.
    Kumar R; Sharma A; Alexiou A; Bilgrami AL; Kamal MA; Ashraf GM
    Front Neurosci; 2022; 16():858126. PubMed ID: 35592264
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Developing Enhanced Blood-Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling.
    Wang W; Kim MT; Sedykh A; Zhu H
    Pharm Res; 2015 Sep; 32(9):3055-65. PubMed ID: 25862462
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods.
    Li H; Yap CW; Ung CY; Xue Y; Cao ZW; Chen YZ
    J Chem Inf Model; 2005; 45(5):1376-84. PubMed ID: 16180914
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.