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Development of the tourism industry in the context of digital transformation

Klimova, T. B., Bogomazova, I. V., Rakhimbekova, Z. S., Yelubayeva, Z. M., Tarakbayeva, R. Y.

Comprehensive analysis of applicability domains of QSPR models for chemical reactions

Rakhimbekova A., Madzhidov T.I., Nugmanov R.I., Gimadiev T.R., Baskin I.I., Varnek A.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Nowadays, the problem of the model’s

Probabilistic approach for virtual screening based on multiple pharmacophores

Madzhidov T.I., Rakhimbekova A., Kutlushuna A., Polishchuk P.

-class machine learning models, and the probability the reflecting model’s confidence can be assigned to a

Conjugated Quantitative Structure-Property Relationship Models: Application to Simultaneous Prediction of Tautomeric Equilibrium Constants and Acidity of Molecules

Zankov D., Madzhidov T., Rakhimbekova A., Gimadiev T., Nugmanov R., Kazymova M., Baskin I., Varnek A.

Highly selective binding of methyl orange dye by cationic water-soluble pillar[5]arenes

Yakimova L., Shurpik D., Gilmanova L., Makhmutova A., Rakhimbekova A., Stoikov I.

SERS investigation of neocuproine adsorption on silver: Influence of electrode potential on methyl groups

Solovyeva E., Rakhimbekova A., Lanchuk Y., Myund L., Denisova A.

Multi-instance learning for structure-activity modeling for molecular properties

Zankov D.V., Shevelev M.D., Nikonenko A.V., Polishchuk P.G., Rakhimbekova A.I., Madzhidov T.I.

SERS investigation of neocuproine adsorption on silver: Influence of electrode potential on methyl groups

Solovyeva E., Rakhimbekova A., Lanchuk Y., Myund L., Denisova A.

Highly selective binding of methyl orange dye by cationic water-soluble pillar[5]arenes

Yakimova L., Shurpik D., Gilmanova L., Makhmutova A., Rakhimbekova A., Stoikov I.

Cross-validation strategies in QSPR modelling of chemical reactions

Rakhimbekova A., Akhmetshin T.N., Minibaeva G.I., Nugmanov R.I., Gimadiev T.R., Madzhidov T.I., Baskin I.I., Varnek A.

Страница 1 из 100502