Hasil Pencarian - Michal Brylinski

Perbaiki Hasil
  1. 1
    eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models.

    eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. oleh Michal Brylinski

    Diterbitkan 2014-09-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  2. 2
    Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks

    Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks oleh Surabhi Maheshwari, Michal Brylinski

    Diterbitkan 2017-05-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  3. 3
    FINDSITE: a threading-based approach to ligand homology modeling.

    FINDSITE: a threading-based approach to ligand homology modeling. oleh Michal Brylinski, Jeffrey Skolnick

    Diterbitkan 2009-06-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  4. 4
    eThread: a highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures.

    eThread: a highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. oleh Michal Brylinski, Daswanth Lingam

    Diterbitkan 2012-01-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  5. 5
    Comparative assessment of strategies to identify similar ligand-binding pockets in proteins

    Comparative assessment of strategies to identify similar ligand-binding pockets in proteins oleh Rajiv Gandhi Govindaraj, Michal Brylinski

    Diterbitkan 2018-03-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  6. 6
    Computational Redesign of Bacterial Biotin Carboxylase Inhibitors Using Structure-Based Virtual Screening of Combinatorial Libraries

    Computational Redesign of Bacterial Biotin Carboxylase Inhibitors Using Structure-Based Virtual Screening of Combinatorial Libraries oleh Michal Brylinski, Grover L. Waldrop

    Diterbitkan 2014-04-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  7. 7
    Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design

    Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design oleh Wentao Shi, Manali Singha, Gopal Srivastava, Limeng Pu, J. Ramanujam, J. Ramanujam, Michal Brylinski, Michal Brylinski

    Diterbitkan 2022-03-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  8. 8
    Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects

    Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects oleh Mengmeng Liu, Gopal Srivastava, J. Ramanujam, Michal Brylinski

    Diterbitkan 2024-01-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  9. 9
    Insights from Augmented Data Integration and Strong Regularization in Drug Synergy Prediction with SynerGNet

    Insights from Augmented Data Integration and Strong Regularization in Drug Synergy Prediction with SynerGNet oleh Mengmeng Liu, Gopal Srivastava, J. Ramanujam, Michal Brylinski

    Diterbitkan 2024-07-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  10. 10
    SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy

    SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy oleh Mengmeng Liu, Gopal Srivastava, J. Ramanujam, Michal Brylinski

    Diterbitkan 2024-02-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  11. 11
    LIMITATION OF CONFORMATIONAL SPACE FOR PROTEINS – EARLY STAGE FOLDING SIMULATION OF HUMAN α AND β HEMOGLOBIN CHAINS

    LIMITATION OF CONFORMATIONAL SPACE FOR PROTEINS – EARLY STAGE FOLDING SIMULATION OF HUMAN α AND β HEMOGLOBIN CHAINS oleh MICHAŁ BRYLIŃSKI, WIKTOR JURKOWSKI, LESZEK KONIECZNY, IRENA ROTERMAN

    Diterbitkan 2004-07-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  12. 12
    Editorial: Structure and function of trans-membrane proteins

    Editorial: Structure and function of trans-membrane proteins oleh Irena Roterman, Michal Brylinski, Fabio Polticelli, Alexandre G. de Brevern

    Diterbitkan 2024-05-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  13. 13
    GraphSite: Ligand Binding Site Classification with Deep Graph Learning

    GraphSite: Ligand Binding Site Classification with Deep Graph Learning oleh Wentao Shi, Manali Singha, Limeng Pu, Gopal Srivastava, Jagannathan Ramanujam, Michal Brylinski

    Diterbitkan 2022-07-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  14. 14
    eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates

    eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates oleh Limeng Pu, Misagh Naderi, Tairan Liu, Hsiao-Chun Wu, Supratik Mukhopadhyay, Michal Brylinski

    Diterbitkan 2019-01-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  15. 15
    An integrated network representation of multiple cancer-specific data for graph-based machine learning

    An integrated network representation of multiple cancer-specific data for graph-based machine learning oleh Limeng Pu, Manali Singha, Hsiao-Chun Wu, Costas Busch, J. Ramanujam, Michal Brylinski

    Diterbitkan 2022-04-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  16. 16
    DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network.

    DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network. oleh Limeng Pu, Rajiv Gandhi Govindaraj, Jeffrey Mitchell Lemoine, Hsiao-Chun Wu, Michal Brylinski

    Diterbitkan 2019-02-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  17. 17
    Prediction of functional sites based on the fuzzy oil drop model.

    Prediction of functional sites based on the fuzzy oil drop model. oleh Michał Bryliński, Katarzyna Prymula, Wiktor Jurkowski, Marek Kochańczyk, Ewa Stawowczyk, Leszek Konieczny, Irena Roterman

    Diterbitkan 2007-05-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  18. 18
    Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2

    Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2 oleh Adam Bess, Frej Berglind, Supratik Mukhopadhyay, Michal Brylinski, Chris Alvin, Fanan Fattah, Kishor M. Wasan

    Diterbitkan 2023-12-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  19. 19
    Hinge Region in DNA Packaging Terminase pUL15 of Herpes Simplex Virus: A Potential Allosteric Target for Antiviral Drugs

    Hinge Region in DNA Packaging Terminase pUL15 of Herpes Simplex Virus: A Potential Allosteric Target for Antiviral Drugs oleh Lana F. Thaljeh, J. Ainsley Rothschild, Misagh Naderi, Lyndon M. Coghill, Jeremy M. Brown, Michal Brylinski

    Diterbitkan 2019-10-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:
  20. 20
    GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

    GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data oleh Guannan Liu, Manali Singha, Limeng Pu, Prasanga Neupane, Joseph Feinstein, Hsiao-Chun Wu, J. Ramanujam, Michal Brylinski

    Diterbitkan 2021-08-01
    Dapatkan teks lengkap
    Artikel
    Simpan ke Daftar
    Disimpan di:

Alat Pencarian: