Асосий контентга ўтиш
AkademIndex

Маҳсулотлар

Ишлаб чиқувчилар учун

AkademBaseЭкотизим учун очиқ API
← Ишга қайтиш

Ушбу ишга иқтибос қилган ишлар

19 та иш

Иш: 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

    МақолаComputational Drug Discovery MethodsJournal of Polymer Research20153 иқтибос
    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

    МақолаComputational Drug Discovery MethodsBioorganic & Medicinal Chemistry20081 иқтибос
    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

    МақолаComputational Drug Discovery MethodsJournal of Computational Chemistry20091 иқтибос
    ABI

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

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

    МақолаComputational Drug Discovery MethodsStructural Chemistry20221 иқтибос
    ABI

  5. QSAR modelling of carcinogenicity by balance of correlations

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

    МақолаAdvanced Chemical Sensor TechnologiesMolecular Diversity20091 иқтибос
    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

    МақолаComputational Drug Discovery MethodsEuropean Journal of Medicinal Chemistry20101 иқтибос
    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

    МақолаPhenothiazines and Benzothiazines Synthesis and ActivitiesComputational Biology and Chemistry20061 иқтибос
    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

    МақолаComputational Drug Discovery MethodsQSAR & Combinatorial Science20061 иқтибос
    ABI

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

    В. О. Кудышкин, Alla P. Toropova

    БобComputational Drug Discovery MethodsChallenges and advances in computational chemistry and physics20231 иқтибос
    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

    МақолаComputational Drug Discovery MethodsMolecular Diversity20090 иқтибос
    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

    МақолаComputational Drug Discovery MethodsJournal of Computational Chemistry20090 иқтибос
    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

    МақолаComputational Drug Discovery MethodsEuropean Journal of Medicinal Chemistry20100 иқтибос
    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

    МақолаComputational Drug Discovery Methods2021 International Conference on Information Science and Communications Technologies (ICISCT)20210 иқтибос
    ABI

  14. Generative Pre-Training from Molecules

    Sanjar Adilov

    ПрепринтMachine Learning in Materials ScienceChemRxiv20210 иқтибос
    ABI

  15. Generative Pre-Training from Molecules

    Sanjar Adilov

    ПрепринтMachine Learning in Materials Science20210 иқтибос
    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 Square20210 иқтибос
    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

    МақолаMachine Learning in Materials ScienceScientific Reports20240 иқтибос
    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

    МақолаAdsorption, diffusion, and thermodynamic properties of materialsSAR and QSAR in environmental research20250 иқтибос
    ABI

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

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

    МақолаNanoparticles: synthesis and applicationsChemical Papers20250 иқтибос
    ABI