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Работы, цитирующие эту работу

Работ: 11

Работа: Beware of q2!

  1. New tyrosinase inhibitors selected by atomic linear indices-based classification models

    Gerardo M. Casañola‐Martín, Mahmud Tareq Hassan Khan, Yovani Marrero‐Ponce +3

    СтатьяComputational Drug Discovery MethodsBioorganic & Medicinal Chemistry Letters2005Цитирований: 1
    ABI

  2. SMILES‐based optimal descriptors: QSAR analysis of fullerene‐based HIV‐1 PR inhibitors by means of balance of correlations

    Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati +2

    СтатьяComputational Drug Discovery MethodsJournal of Computational Chemistry2009Цитирований: 1
    ABI

  3. QSAR modelling of carcinogenicity by balance of correlations

    Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati +1

    СтатьяAdvanced Chemical Sensor TechnologiesMolecular Diversity2009Цитирований: 1
    ABI

  4. Atom‐Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors: Results of <i>In Silico</i> Studies Supported by Experimental Results

    Yovani Marrero Ponce, Mahmud Tareq Hassan Khan, Gerardo M. Casañola‐Martín +5

    СтатьяComputational Drug Discovery MethodsQSAR & Combinatorial Science2006Цитирований: 1
    ABI

  5. Bond-based 2D TOMOCOMD-CARDD approach for drug discovery: aiding decision-making in ‘in silico’ selection of new lead tyrosinase inhibitors

    Yovani Marrero‐Ponce, Mahmud Tareq Hassan Khan, Gerardo M. Casañola‐Martín +5

    СтатьяComputational Drug Discovery MethodsJournal of Computer-Aided Molecular Design2007Цитирований: 0
    ABI

  6. QSPR modeling of enthalpies of formation for organometallic compounds by SMART‐based optimal descriptors

    Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati +1

    СтатьяComputational Drug Discovery MethodsJournal of Computational Chemistry2009Цитирований: 0
    ABI

  7. InChI-based optimal descriptors: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors by correlation balance

    Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati +2

    СтатьяComputational Drug Discovery MethodsEuropean Journal of Medicinal Chemistry2010Цитирований: 0
    ABI

  8. The Monte Carlo Method Based on Eclectic Data as an Efficient Tool for Predictions of Endpoints for Nanomaterials - Two Examples of Application

    Andrey A. Toropov, Alla P. Toropova, Aleksandar M. Veselinović +8

    СтатьяComputational Drug Discovery MethodsCombinatorial Chemistry & High Throughput Screening2015Цитирований: 0
    ABI

  9. Mathematical modeling of refractive index based on machine learning (kNN-QSPR) method

    Rifkat Davronov, Bakhtiyor Rasulev, Fatima Аdilova

    СтатьяComputational Drug Discovery Methods2020Цитирований: 0
    ABI

  10. Quantitative Structure─Permittivity Relationship Study of a Series of Polymers

    Yevhenii Zhuravskyi, Kweeni Iduoku, Meade Erickson +8

    СтатьяDielectric materials and actuatorsACS Materials Au2024Цитирований: 0
    ABI

  11. Predictive modeling for compressive strength of blended cement concrete using hybrid machine learning models

    Asad Ullah Khan, Raheel Asghar, Najmul Hassan +4

    СтатьяInnovative concrete reinforcement materialsMultiscale and Multidisciplinary Modeling Experiments and Design2024Цитирований: 0
    ABI