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Работы, цитирующие эту работу

Работ: 19

Работа: SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

  1. Prediction of the Q-e parameters from structures of transfer chain agents

    Alla P. Toropova, Andrey A. Toropov, В. О. Кудышкин +1

    СтатьяComputational Drug Discovery MethodsJournal of Polymer Research2015Цитирований: 3
    ABI

  2. Additive SMILES-based optimal descriptors in QSAR modelling bee toxicity: Using rare SMILES attributes to define the applicability domain

    Andrey A. Toropov, Emilio Benfenati

    СтатьяComputational Drug Discovery MethodsBioorganic & Medicinal Chemistry2008Цитирований: 1
    ABI

  3. 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

  4. The system of self-consistent QSPR-models for refractive index of polymers

    Andrey A. Toropov, Alla P. Toropova, В. О. Кудышкин

    СтатьяComputational Drug Discovery MethodsStructural Chemistry2022Цитирований: 1
    ABI

  5. QSAR modelling of carcinogenicity by balance of correlations

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

    СтатьяAdvanced Chemical Sensor TechnologiesMolecular Diversity2009Цитирований: 1
    ABI

  6. QSPR modeling of octanol/water partition coefficient of antineoplastic agents by balance of correlations

    Andrey A. Toropov, Alla P. Toropova, Ivan Raška +1

    СтатьяComputational Drug Discovery MethodsEuropean Journal of Medicinal Chemistry2010Цитирований: 1
    ABI

  7. QSPR modeling of the half-wave potentials of benzoxazines by optimal descriptors calculated with the SMILES

    Andrey A. Toropov, Karel Nesměrák, Ivan Raška +2

    СтатьяPhenothiazines and Benzothiazines Synthesis and ActivitiesComputational Biology and Chemistry2006Цитирований: 1
    ABI

  8. QSAR Modeling of Peripheral<i>Versus</i>Central Benzodiazepine Receptor Binding Affinity of 2‐Phenylimidazo[1,2‐a]pyridineacetamides using Optimal Descriptors Calculated with SMILES

    Kunal Roy, Andrey A. Toropov, Ivan Raška

    СтатьяComputational Drug Discovery MethodsQSAR & Combinatorial Science2006Цитирований: 1
    ABI

  9. Building Up QSPR for Polymers Endpoints by Using SMILES-Based Optimal Descriptors

    В. О. Кудышкин, Alla P. Toropova

    ГлаваComputational Drug Discovery MethodsChallenges and advances in computational chemistry and physics2023Цитирований: 1
    ABI

  10. QSAR-modeling of toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors

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

    СтатьяComputational Drug Discovery MethodsMolecular Diversity2009Цитирований: 0
    ABI

  11. 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

  12. 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

  13. Predicting the activity and properties of chemicals based on RoBERTa

    Davronov Rifqat Rahimovich, Safarov Ro'zmat Abdiqayum O'g'li, Abdumalikov Shoxrux Qaxramon O'g'li

    СтатьяComputational Drug Discovery Methods2021 International Conference on Information Science and Communications Technologies (ICISCT)2021Цитирований: 0
    ABI

  14. Generative Pre-Training from Molecules

    Sanjar Adilov

    ПрепринтMachine Learning in Materials ScienceChemRxiv2021Цитирований: 0
    ABI

  15. Generative Pre-Training from Molecules

    Sanjar Adilov

    ПрепринтMachine Learning in Materials Science2021Цитирований: 0
    ABI

  16. The System of Self-Consistent QSPR-Models for Refractive Index of Polymers

    Andrey A. Toropov, Alla P. Toropova, В. О. Кудышкин

    ПрепринтComputational Drug Discovery MethodsResearch Square2021Цитирований: 0
    ABI

  17. GrapheNet: a deep learning framework for predicting the physical and electronic properties of nanographenes using images

    Tommaso Forni, Matteo Baldoni, Fabio Le Piane +1

    СтатьяMachine Learning in Materials ScienceScientific Reports2024Цитирований: 0
    ABI

  18. Application of monomer structures and fragments of local symmetry for simulation of glass transition temperatures of polymers

    Alla P. Toropova, Andrey A. Toropov, В. О. Кудышкин +2

    СтатьяAdsorption, diffusion, and thermodynamic properties of materialsSAR and QSAR in environmental research2025Цитирований: 0
    ABI

  19. Robust classification frameworks for oxide nanomaterials assisted by machine learning

    Wei Ju, Usama S. Altimari, Raman Kumar +9

    СтатьяNanoparticles: synthesis and applicationsChemical Papers2025Цитирований: 0
    ABI