Rasulev Research Group

Welcome to the Computational Polymer Science and Cheminformatics Group


  1. Karuth A., Alesadi A., Vashisth A., Xia W., Rasulev B.,  A Reactive Molecular Dynamics Study of Hygrothermal Degradation of Crosslinked Epoxy Polymers,  ACS Applied Polymer Materials, 2022, 4, 6, 4411-4423  DOI: 10.1021/acsapm.2c00383
  2. Erickson M., Han Y., Rasulev B., Kilin D.S. Photoinduced Degradation of Polymeric Chains, Journal of Physical Chemistry Letters, 2022, 13, 19, 4374–4380  DOI: 10.1021/acs.jpclett.2c00802
  3. Varghese G.P.J., David D.A., Karuth A., Jabeen F.M.J, Begum P.M.S., George J.J.,  Rasulev B., Raghavan P. Experimental and Simulation Studies on Nonwoven Polypropylene–Nitrile Rubber Blend: Recycling of Medical Face Masks to an Engineering Product,  ACS Omega, 2022,  7, 6, 4791–4803, DOI: 10.1021/acsomega.1c04913
  4. Patnode K., Rasulev B., Voronov A. Synergistic Behavior of Plant Proteins and Biobased Latexes in Bioplastic Food Packaging Materials: Experimental and Machine Learning Study, ACS Applied Materials and Interfaces, 2022, 14, 6, 8384–8393, DOI:10.1021/acsami.1c21650
  5. González-Díaz H.,  Rasulev B., Diéguez-Santana K., Towards Rational Nanomaterial Design by Prediction of Drug-Nanoparticle Systems Interaction vs. Bacteria Metabolic Networks, Environmental Science: Nano, 2022, 9, 1391-1413,  DOI:10.1039/D1EN00967B
  6. Fjodorova N., Novič M., Venko K., Drgan V., Rasulev B., Saçan M.T., Erdem S.S., Tugcu G., Toropova A.P., Toropov A.A. How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases, Computational and Structural Biotechnology Journal, 2022, 20, 913-924 DOI: 10.1016/j.csbj.2022.02.006
  7. Diéguez-Santana K., Casañola-Martin G., Rasulev B., Green J.R., González-Díaz H., Machine Learning Mapping of Metabolic Networks vs. ChEMBL Data of Antibacterial Compounds, Molecular Pharmaceutics, 2022, 19, 7, 2151–2163  DOI: 10.1021/acs.molpharmaceut.2c00029
  8. Daghighi A., Casanola-Martin G.M., Timmerman T.,  Milenković D., Lucic B.,  Rasulev B. In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach, Toxics, 2022, 10(12), 746 DOI: 10.3390/toxics10120746
  9. Usmanov D., Azamatov A., Baykuziyev T., Yusupova U., Rasulev B. Chemical constituents, anti-inflammatory and analgesic activities of iridoids preparation from Phlomoides labiosa bunge, Natural Product Research, 2022, 1-5 DOI: 10.1080/14786419.2022.2104274
  10. Timmerman T., Sun D., Rasulev B. Automating protein structure homology modeling in exosome protein samples to predict beta sheet content, SoftwareX, 2022, in preparation
  11. Kayumov J., Usmanov D., Rasulev B., A Machine Learning-based Quantitative Structure-Activity Relationship Study for the Carcinogenic Activity of Phenylethylamine, PEDAGOGS, 2022, 8 (2) 2022, 227-232
  12. Karabulut S., Mansour B., Rasulev B.,  Gauld J.W., The Hydrolysis Rate of Paraxonase-1 Q and R Isoenzymes: An in silico Study Based on in vitro Data, Molecules, 2022, 27(20),  6780; DOI: 10.3390/molecules27206780
  13. Diéguez-Santana K.,  Puris A., Rivera-Borroto O.M., Casanola-Martin G., Rasulev B., Gonzalez-Diaz H. A Fuzzy System Classification Approach for QSAR modeling of α-amylase and α-glucosidase inhibitors, Current Computer-Aided Drug Design, 2022, DOI: 10.2174/1573409918666220929124820
  14. Simsek T., Gorb L., Mayer C., Rasulev B., Simsek S. Investigation of inclusion complexes of selected Flavors with alpha– and beta-Cyclodextrins, 2022, in preparation
  15. Casanola G.M., Karuth A., Pham-The H., Webster D.C., Rasulev B., Machine Learning Analysis of a Large Set of Homopolymers to Predict Glass Transition Temperatures, 2022, in preparation
  16. Iduoku K., Ngongang M., Simsek M., Dona J.M., Gorb L., Simsek S., Rasulev B.  A Quantitative Structure-Activity Relationship Analysis of Natural Phenolic Compounds towards beta-Cyclodextrin, 2022, in preparation
  17. Toropova A.P., Toropov A.A., Fjodorova N., Mikolajczyk A., Rasulev B. Nano-QSAR-model for Binding Activity of Fullerene Derivatives, 2022, in preparation
  18. Karuth A., Lystrom L., Sun W., Kilin D., Kilina S., Rasulev B.  Combined Experimental and Computational Structure-Property Study of Organometallic Complexes with near-IR emission, 2022, in preparation
  19. Zhuravskyi Y., Iduoku K., Erickson M., Anas K., Usmanov D., Rasulev B.  A Quantitative Structure-Permittivity Relationship Analysis of a Series of Polymers, 2022, in preparation
  20. Erickson M., Kar S., Leszczynski J., Rasulev B. Prediction of Molar Thermal Decomposition for a Diverse Polymers Using Quantitative Structure-Property Relationship Analysis, 2022, in preparation


  1. Patnode K., Demchuk Z., Johnson S.,  Voronov A., Rasulev B. Computational Protein-Ligand Docking and Experimental Study of Bioplastic Films from Soybean Protein, Zein and Natural Modifiers, ACS Sustainable Chemistry and Engineering, 2021, 9, 10740-10748, DOI: 10.1021/acssuschemeng.1c01202
  2. Anas K., Amirhadi A., Xia W., Rasulev B. Predicting Glass Transition of Amorphous Polymers via Integration of Cheminformatics and Molecular Dynamics Simulations, Polymer, 2021, 218, 123495 DOI: 10.1016/j.polymer.2021.123495
  3. Diéguez-Santana K., Casañola-Martin G.M., Green J.R., Rasulev B., González-Díaz H. Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models, Current Topics in Medicinal Chemistry, 2021, 21 (9), 819-827 DOI: 10.2174/1568026621666210331161144
  4. Han Y., Iduoku K., Rasulev B., Leontyev A., Hobbie E.K., Tretiak S., Kilina S.V., Kilin D.S. Hot Carrier Dynamics at Ligated Silicon (111) Surfaces: a Computational Study, Journal of Physical Chemistry Letters, 2021, 12, 7504-7511 DOI: 10.1021/acs.jpclett.1c02084
  5. Usmanov D, Yusupova U, Syrov V, Ramazonov N, Rasulev B. Iridoid glucosides and triterpene acids from Phlomis linearifolia, growing in Uzbekistan and its hepatoprotective activity. Natural Product Research, 2021, 35 (14), 2449-2453 DOI: 10.1080/14786419.2019.1677650
  6. Davronov R, Rasulev B., Adilova F., Mathematical modeling of refractive index based on machine learning (kNN-QSPR) method, Proceedings of IEEE 14th International Conference on Application of Information and Communication Technologies (AICT), 2020, pp. 1-3, DOI: 10.1109/AICT50176.2020.9368648.


  1. Erickson M.E., Rasulev B. A Refractive Index Study of a Diverse Set of Polymeric Materials by Combined Quantum-Chemical and QSPR Approach, Molecules, 2020, 25 (17), 3772
  2. Serdiuk V., Shogren K.L., Kovalenko T., Rasulev B., Yaszemski M., Maran A., Voronov A. Detection of Macromolecular Inversion-Induced Structural Changes in Osteosarcoma Cells by FTIR Microspectroscopy, Analytical and Bioanalytical Chemistry, 2020, 412 (26), 7253-7262
  3. Fjodorova N., Novič M., Venko K., Rasulev B. A Comprehensive Cheminformatics Analysis of Structural Features Affecting the Binding Activity of Fullerene Derivatives, Nanomaterials, 2020, 10(1), 90
  4. Sigurnjak M, Ukić Š, Cvetnić M, Markić M, Stankov MN, Rasulev B, Kušić H, Božić AL, Rogošić M, Bolanča T. Combined Toxicities of Binary Mixtures of alachlor, chlorfenvinphos, diuron and isoproturon. Chemosphere, 2020, 240, 124973.
  5. Simsek T., Rasulev B., Mayer C., Simsek S. Preparation and characterization of inclusion complexes of β-cyclodextrin and phenolics from wheat bran by combination of experimental and computational techniques, Molecules, 2020, 25 (18), 4275
  6. Rasulev B., Casanola-Martin G., QSAR/QSPR in Polymers: Recent Developments in Property Modeling, International Journal of Quantitative Structure-Property Relationships, 2020, 5(1), 80-88
  7. Usmanov D., Rasulev B., Syrov V., Yusupova U., Ramazonov N. Structure-Hepatoprotective Activity Relationship Study of Iridoids: A QSAR Analysis, International Journal of Quantitative Structure-Property Relationships, 2020, 5(3), 48-58


  1. Petrosyan L.S., Sizochenko N., Leszczynski J., Rasulev B. Modeling of Glass Transition Temperatures for Polymeric Coating Materials: Application of QSPR Mixture‐based Approach, Molecular Informatics, 2019, DOI: 10.1002/minf.201800150
  2. Ilardo M, Bose R, Meringer M, Rasulev B, Grefenstette N, Stephenson J, Freeland S, Gillams RJ, Butch CJ, Cleaves HJ. Adaptive properties of the Genetically Encoded Amino Acid Alphabet are Inherited from its Subsets. Scientific Reports, 2019, 9(1), 12468.
  3. Sizochenko N, Syzochenko M, Fjodorova N, Rasulev B, Leszczynski J. Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques. Ecotoxicology and Environmental Safety, 2019, 185, 109733.
  4. Diéguez‐Santana K., Rivera‐Borroto O.M., Puris A., Pham‐The H., Le‐Thi‐Thu H.,  Rasulev B., Casañola‐Martin G.M. Beyond Model Interpretability using LDA and Decision Trees for α‐Amylase and α‐Glucosidase Inhibitor Classification Studies
    Chemical Biology & Drug Design, 2019, DOI: 10.1111/cbdd.13518
  5. Cvetnic M., Perisic D.J., Kovacic M., Ukic S., Bolanca T., Rasulev B., Kusic H., Lončarić Božić A. Toxicity of aromatic pollutants and photooxidative intermediates in water: A QSAR study, Ecotoxicology and Environmental Safety, 2019, 169, 918-927
  6. Simsek, T., Simsek, S., Mayer, C., Rasulev, B. Combined Computational and Experimental  Study on the Inclusion Complexes of β-Cyclodextrin with Selected Food Phenolic Compounds, Structural Chemistry, 2019, DOI: 10.1007/s11224-019-01347-4
  7. Cvetnic M., Novak Stankov M., Kovacic M., Ukic S., Bolanca T., Kusic H., Rasulev B., Dionysiou D.D., Loncaric Bozic A., Key structural features promoting radical driven degradation of emerging contaminants in water, Environmental International, 2019, 124, 38-48
  8. Mikolajczyk A., Sizochenko N., Mulkiewicz E., Malankowska A., Rasulev B., Puzyn T. Chemoinformatics Approach for Characterization of Hybrid Nanomaterials: Safer and Efficient Design Perspective, Nanoscale, 2019, 11, 11808-11818 DOI: 10.1039/C9NR01162E
  9. Ukić Š, Sigurnjak M, Cvetnić M, Markić M, Stankov MN, Rogošić M, Rasulev B, Božić AL, Kušić H, Bolanča T. Toxicity of pharmaceuticals in binary mixtures: Assessment by additive and non-additive toxicity models. Ecotoxicology and Environmental Safety, 2019, 185, 109696.


  1. Chitemere R.P., Stafslien S., Rasulev B., Webster D.C., Quadir M. Soysome: A surfactant-free, fully biobased, self-assembled platform for nanoscale drug delivery applications, ACS Applied Bio Materials, 2018, 1 (6), 1830-1841 DOI: 10.1021/acsabm.8b00317
  2. Mikolajczyk, A., Gajewicz, A., Mulkiewicz, E., Rasulev, B., Marchelek, M., Diak, M., Hirano, S., Zaleska-Medynska, A., Puzyn, T., Nano-QSAR modeling for eco-safe design of second generation TiO2-based nano-photocatalysts, Environmental Science: Nano, 2018,  5 (5), 1150-1160. DOI: 10.1039/C8EN00085A
  3. Chen M., Jabeen F., Rasulev B., Ossowski M., Boudjouk P. A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers, Journal of Polymer Science, Part B: Polymer Physics, 2018, 56 (11), 877-885. DOI:10.1002/polb.24602
  4. Sizochenko, N., Mikolajczyk, A., Jagiello, K., Puzyn, T., Leszczynski, J., Rasulev, B. How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach. Nanoscale, 2018, 10, 582-591. DOI: 10.1039/C7NR05618D
  5. Golbamaki A., Golbamaki N., Sizochenko N., Rasulev B., Leszczynski J., Benfenati E. Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties, Nanotoxicology, 2018, in press, DOI: 10.1080/17435390.2018.1478999
  6. Cabrera-Pérez M.Á., Nam N.H., Castillo-Garit J.A., Rasulev B., Le-Thi-Thu H.,Casañola-Martin G.M. In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling, Current Topics in Medicinal Chemistry, 2018, 18 (26), 2209-2229
  7. Khan P.M., Rasulev B., Roy K. QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors, ACS Omega, 2018, 3 (10), 13374-13386
  8. Cvetnić M., Stankov M.N., Ukić Š ., Božić A.L., Kušić H., Rasulev B., Bolanča T. AOP degradation of emerging contaminants in water: Prediction of second order kinetics by QSPR modeling, AIP Conference Proceedings, 2018, 2040 (1), 150005 DOI: 10.1063/1.5079208


  1. Rasulev B., Jabeen F.J., Stafslien S., Chisholm B.J., Bahr J., Ossowski M., Boudjouk P.R., Polymer coating materials and their fouling release activity: A cheminformatics approach to predict properties, ACS Applied Materials & Interfaces, 2017, 9 (2), 1781–1792
  2. Jabeen, F., Chen, M., Rasulev, B., Ossowski, M., Boudjouk, P. Refractive indices of diverse data set of polymers: A computational QSPR based study, Computational Materials Science, 2017, 137, 215-224
  3. Han, Y., Rasulev, B., Kilin, D.S. Photofragmentation of Tetranitromethane: Spin-Unrestricted Time-Dependent Excited-State Molecular Dynamics, The Journal of Physical Chemistry Letters, 2017, 8 (14), 3185–3192
  4. Han, Y., Meng, Q., Rasulev, B., May, P.S., Berry, M.T.,  Kilin D.S. Photoinduced Charge Transfer versus Fragmentation Pathways in Lanthanum Cyclopentadienyl Complexes, Journal of Chemical Theory and Computation, 2017, 13 (9), 4281-4296
  5. Fjodorova N., Novic M., Gajewicz A., Rasulev B. The way to cover prediction for cytotoxicity of all existing nano-sized metal oxides by using neural network method, Nanotoxicology, 2017, 11(4), 475-483. DOI: 10.1080/17435390.2017.1310949
  6. Gooch A, Sizochenko N, Rasulev B, Gorb L, Leszczynski J. In Vivo Toxicity of Nitroaromatics: A Comprehensive QSAR Study, Environmental Toxicology and Chemistry, 2017, DOI: 10.1002/etc.3761
  7. Lucky Ahmed, B. Rasulev, S. Kar, J. Leszczynski, Inhibitors or Toxins? Large Library Target-Specific Screening of Fullerene-based Nanoparticles for Drug Design Purposes, Nanoscale, 2017, 9, 10263-10276
  8. González-Durruthy, M., Monserrat, J.M., Rasulev, B., Casañola-Martín, G.M., Barreiro Sorrivas, J.M., Paraíso-Medina, S., Maojo, V., González-Díaz, H., Pazos, A. and Munteanu, C.R. Carbon nanotubes’ effect on mitochondrial oxygen flux dynamics: polarography experimental study and machine learning models using star graph trace invariants of Raman spectra. Nanomaterials, 20177(11), 386.
  9. Petrosyan L.S., Kar S., Leszczynski J., Rasulev B. Exploring Simple, Interpretable, and Predictive QSPR Model of Fullerene C60 Solubility in Organic Solvents, Journal of Nanotoxicology and Nanomedicine (JNN), 2017, 2(1), 28-43 DOI: 10.4018/JNN.2017010103


  1. Golbamaki A., Golbamaki N., Sizochenko N., Rasulev B., Cassano A., Puzyn T., Leszczynski J., Benfenati E. Classification nano-SAR modeling of metal oxides nanoparticles genotoxicity based on comet assay data, Toxicology Letters, 2016, 258, S271
  2. Jagiello, K., Grzonkowska, M., Swirog, M., Ahmed, L., Rasulev, B., Avramopoulos, A., Papadopoulos, M.G., Leszczynski, J., Puzyn, T. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives, Journal of Nanoparticle Research, 2016, 18:256
  3. Yilmaz, H., Ahmed, L., Rasulev, B., Leszczynski, J. Application of ligand- and receptor-based approaches for prediction of the HIV-RT inhibitory activity of fullerene derivatives, Journal of Nanoparticle Research, 2016, 18(5):123
  4. Watkins, M., Sizochenko, N.,·Rasulev, B., Leszczynski, J. Estimation of melting points of large set of persistent organic pollutants utilizing QSPR approach, Journal of Molecular Modeling, 2016, 22(3), 55
  5. Antypenko O.M., Kovalenko S., Rasulev B., Leszczynski J. Synthesis of 6-NR-Tetrazolo [1, 5-c] quinazolin-5 (6H)-ones and Their Anticancer Activity, Acta Chimica Slovenica, 2016, 63 (3), 638-645


  1. Golbamaki N., Rasulev B., Cassano A., Robinson R.L.M., Leszczynski J., Benfenati E., Cronin M.T.D., Genotoxicity of metal oxide nanomaterials: Review of Recent data and discussion of possible mechanisms, Nanoscale, 2015, 7(6), 2154-2168
  2. Ilardo M, Meringer M, Freeland S, Rasulev B, Cleaves HJ II. Extraordinarily adaptive properties of the genetically encoded amino acids, Scientific Reports, 2015, 5, 9414
  3. Sizochenko N., Rasulev B., Gajewicz A., Mokshyna E., Kuz’min VE, Leszczynski J., Puzyn T. Causal inference methods to assist in mechanistic interpretation of classification nano-SAR models, RSC Advances, 2015, 5 (95), 77739-77745
  4. Gajewicz A., Cronin M.T.D., Rasulev B., Leszczynski J., Puzyn T., Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across, Nanotechnology, 2015, 26, 015701
  5. Gajewicz A., Schaeublin N., Rasulev B., Hussain S., Leszczynska D., Puzyn T., Leszczynski J. Towards Understanding Mechanisms Governing Cytotoxicity of Metal Oxides Nanoparticles: Hints from Nano-QSAR Studies, 2015, Nanotoxicology, 2015, 9(3), 313-325
  6. Mikolajczyk A, Gajewicz A., Rasulev B.,Schaeublin N., Maurer-Gardner E., Hussain S., Leszczynski L., Puzyn T., Zeta Potential for Metal Oxide Nanoparticles: A Predictive Model Developed by a Nano-Quantitative Structure−Property Relationship Approach, Chemistry of Materials, 2015, 27(7), 2400-2407
  7. Reis H., Rasulev B., Papadopoulos M.G. and Leszczynski J., Reliable but Timesaving: In Search of an Efficient Quantum-chemical Method for the Description of Functional Fullerenes, Current Topics in Medicinal Chemistry, 2015, 15, 1845-1858
  8. Yilmaz H., Rasulev B., Leszczynski J., Modeling the Dispersibility of Single Walled Carbon Nanotubes in Organic Solvents by Quantitative Structure-Activity Relationship Approach, Nanomaterials, 2015, 5, 778-791
  9. Han, Y., Meng, Q., Rasulev, B., Kilin, D.S. Photofragmentation of Gas-Phase Lanthanum Iso-propyl-cyclopentadienyl Complex: Computational Modeling vs. Experiment, Journal of Physical Chemistry A, 2015, 119(44), 10838
  10. Yilmaz H., Sizochenko N., Rasulev B., Toropov A., Guzel Y., Kuz’min V., Leszczynska D., Leszczynski J. Journal of Food and Drug Analysis, 2015, 23 (2), 168-175


  1. Turabekova M., Rasulev B., Dzhakhangirov F.N., Toropov A.A., Leszczynska D., Leszczynski J., Aconitum and Delphinium Diterpenoid Alkaloids of Local Anesthetic Activity: Comparative QSAR Analysis Based on GA-MLRA/PLS and Optimal Descriptors Approach, Journal of Environmental Science and Health Part C, 2014, 32, 1-26
  2. Sizochenko N., Rasulev B., Gajewicz A., Kuzmin VE, Puzyn T., Leszczynski J. From Basic Physics to Mechanisms of Toxicity: Liquid Drop Approach Applied to Develop Predictive Classification Models for Toxicity of Metal Oxide Nanoparticles, 2014, Nanoscale, 6(22), 13986-13993
  3. Turabekova M.A., Rasulev B., Theodor M., Jackman J., Leszczynska, D., Leszczynski, J. Immunotoxicity of nanoparticles: Computational study suggests that CNTs and C60 fullerenes might be recognized as pathogens by Toll-like receptors, 2014, Nanoscale, 6, 3488
  4. Juretic D., Kusic H., Dionysiou D.D., Rasulev B., Bozic A.L., Modeling of photooxidative degradation of aromatics in water matrix; combination of mechanistic and structural-relationship approach, Chemical Engineering Journal, 2014, 257, 229-241
  5. Golbamaki N. Bakhtyari, Golbamaki A. Bakhtyari, Benfenati E., Cronin M., Rasulev B., Leszczynski J., Prediction of Genotoxicity of Nano Metal Oxides by Computational Methods: A New Decision Tree QSAR Model, Environmental and Molecular Mutagenesis, 2014, 55, S43-S43
  6. Mozolewska M., Krupa P., Rasulev B., Liwo A., Leszczynski J. Preliminary Studies of Interaction between Nanotubes and Toll-Like Receptors, Task Quarterly, 2014, 18 (4), 351-355
  7. Krupa P., Mozolewska M., Rasulev B., Czaplewski C., Leszczynski J. Towards Mechanisms of Nanotoxicity – Interaction of Gold Nanoparticles with Proteins and DNA, Task Quarterly, 2014, 18 (4), 337-341


  1. Ahmed L., Rasulev B., Turabekova M., Leszczynska D., Leszczynski J. Receptor- and ligand-based study of fullerene analogues: comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations, Organic & Biomolecular Chemistry, 2013, 11, 5798–5808
  2. Golbamaki N. Bakhtyari, Rasulev B., Leszczynski J., Benfenati E., Cronin M., Genotoxicity of Metal Oxide Nanoparticles: A New Predictive (Q) SAR Model, Environmental and Molecular Mutagenesis, 2013, 54, S48-S48


  1. Gajewicz A., Rasulev B., Dinadayalane T., Urbaszek P., Puzyn T., Leszczynska D., Leszczynski J. Advancing risk assessment of engineered nanomaterials: Application of computational approaches,  Advanced Drug Delivery Reviews, 2012, 64 (15), 1663-1693 (Impact Factor – 13.5)
  2. Toropov A.A., Toropova A.P., Rasulev B.F., Benfenati E., Gini G., Leszczynska D., Leszczynski J. Coral: CORAL: Binary Classifications (Active/Inactive) for Liver-Related Adverse Effects of Drugs,Current Drug Safety, 2012, 7(4), 257-261
  3. Rasulev B.F., Watkins M., Theodore M., Jackman J., Lesczynska D., Leszczynski J., Structures of gold clusters in the size range 2-2016 atoms. A quantum-chemical study by Extended Huckel and DFT, Nanoscience & Nanotechnology-A, 2012, 2(1), 2-10
  4. Primera-Pedrozo O.M., Arslan Z., Rasulev B., Leszczynski J., Room temperature synthesis of PbSe quantum dots in aqueous solution: Stabilization by interactions with ligands, Nanoscale, 2012, 4, 1312-1320
  5. Toropov A.A., Toropova A.P., Rasulev B.F., Benfenati E., Gini G., Leszczynska D., Leszczynski J. Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical,Journal of Computational Chemistry, 2012, 33 (23), 1902-1906
  6. Toropova A.P., Toropov, A.A., Rasulev, B.F., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J. QSAR models for ACE-inhibitor activity of tri-peptides based on representation of the molecular structure by graph of atomic orbitals and SMILES, Structural Chemistry, 2012, 23(6), 1873-1878
  7. Rasulev, B., Turabekova, M., Gorska, M., Kulig, K., Bielejewska, A., Lipkowski, J., Leszczynski, J. Use of quantitative structure-enantioselective retention relationship for the liquid chromatography chiral separation prediction of the series of pyrrolidin-2-one compounds, Chirality, 2012, 24(1), 72-77


  1. Puzyn T., Rasulev B., Gajewicz A., Hu X., Dasari T.P., Michalkova A., Hwang H.M., Toropov A., Leszczynska D., Leszczynski J. Using Nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles, Nature Nanotechnology, 2011, 6, 175-178 (Impact Factor – 34.1)
  2. Petrova T., Rasulev B.F., Toropov A.A., Leszczynska D., Leszczynski J. Improved Model for Fullerene C60 Solubility in Organic Solvents Based on Quantum-Chemical and Topological Descriptors, Journal of Nanoparticle Research, 2011, 13 (8), 3235-3247
  3. Turabekova, M.A., Vinogradova, V.I., Werbovetz, K.A., Capers, J., Rasulev, B.F., Levkovich, M.G., Rakhimov, S.B., Abdullaev, N.D, Structure-activity relationship investigations of leishmanicidal N-benzylcytisine derivatives, Chemical Biology and Drug Design, 2011, 78 (1), pp. 183-189


  1. Cook S.M., Aker W.G., Rasulev B.F., Hwang H.-M., Leszczynski J., Jenkins J.J., Shockley V. Choosing safe dispersing media for C60 fullerenes by using cytotoxicity tests on the bacterium Escherichia coli, Journal of Hazardous Materials, 2010, 176 (1-3), 367-373
  2. Rasulev B.F., Kušic H., Lesczynska D., Leszczynski J., Koprivanac N. QSAR modeling of acute toxicity on mammals for aromatic compounds: the case study using oral LD50 for rats, Journal of Environmental Monitoring, 2010, 12 (5), 1037-1044
  3. Turabekova, M.A., Rasulev, B.F., Dzhakhangirov, F.N., Leszczynska, D., Leszczynski, J., Aconitum and Delphinium alkaloids of curare-like activity. QSAR analysis and molecular docking of alkaloids into AChBP, European Journal of Medicinal Chemistry, 2010, 45 (9), 3885-3894


  1. Kušić H., Rasulev B., Leszczynska D., Leszczynski J., Koprivanac N. Prediction of Rate Constants for Radical Degradation of Aromatic Pollutants in Water Matrix: A QSAR Study, Chemosphere, 2009, 75, 1128–1134
  2. Paukku Y., Rasulev B.F., Syrov V., Khushbaktova Z., Leszczynski J. Structure-Hepatoprotective Activity Relationship Study of Sesquiterpene Lactones: A QSAR Analysis, International Journal of Quantum Chemistry, 2009, 109, 17–27


  1. Rasulev B.F., Toropov A.A., Hamme A.T., Leszczynski J., Multiple Linear Regression Analysis and Optimal Descriptors: Predicting the Cholesteryl Ester Transfer Protein Inhibition Activity, QSAR and Combinatorial Science, 2008, 27 (5), 595-606
  2. Toropov A.A., Rasulev B.F., Leszczynski J. QSAR modeling of acute toxicity by balance of correlations, Bioorganic and Medicinal Chemistry, 2008, 16, 5999–6008.
  3. Toropov A.A., Rasulev B.F., Leszczynska D., Leszczynski J., Multiplicative SMILES Based Optimal Descriptors: QSPR Modeling of Fullerene C60 Solubility in Organic Solvents, Chemical Physics Letters, 2008, 457, 332–336
  4. Turabekova M.A., Rasulev B.F., Levkovich M.G., Abdullaev N.D. and Leszczynski J. Aconitum and Delphinium sp. Alkaloids as Antagonist Modulators of Voltage-Gated Na+ Channels. AM1/DFT Electronic Structure Investigations and QSAR Studies. Computational Biology and Chemistry, 2008, 32, 88-101
  5. Turabekova M.A., Rasulev B.F., Dzhakhangirov F.N. and Salikhov Sh.I., Aconitum and Delphinium alkaloids. “Drug-likeness” descriptors related to toxic mode of action, Environmental Toxicology and Pharmacology, 2008, 25, 310-320
  6. MA Turabekova, VI Vinogradova, BF Rasulev, MG Levkovich, K Werbovetz, J Capers, ND Abdullaev, Antiparasitic activity of certain isoquinoline alkaloids and their hypothetical complexes with oligonucleotides, Chemistry of Natural Compounds, 44 (3), 341-345


  1. Rasulev B.F., Saidkhodzhaev A.I., Nazrullaev S.S., Akhmedkhodzhaeva K.S., Khushbaktova Z.A., Leszczynski J., Molecular modeling and QSAR analysis of the estrogenic activity of terpenoids isolated from Ferula plants, SAR & QSAR in Environmental Research, 2007, 18 (7-8), 663-673
  2. Toropov A.A., Rasulev B.F., Leszczynska D., Leszczynski J., Additive SMILES based optimal descriptors: QSPR modeling of fullerene C60 solubility in organic solvents, Chemical Physics Letters, 2007, 444, 209-214
  3. Toropov A.A., Rasulev B.F., Leszczynski J., QSAR Modeling of Acute Toxicity for Nitrobenzene Derivatives towards Rats: Comparative analysis by MLRA and Optimal Descriptors, QSAR and Combinatorial Science, 2007, 26 (5), 686-693


  1. Isayev O., Rasulev B.F., Gorb L. and Leszczynski J, Structure-Toxicity Relationships of Nitroaromatic Compounds, Molecular Diversity, 2006, 10 (2) 233-245


  1. Rasulev B.F., Abdullaev N.D., Syrov V.N., Leszczynski J., A Quantitative Structure-Activity Relationship (QSAR) Study of the Antioxidant Activity of Flavonoids, QSAR and Combinatorial Science, 2005, 24, 9, 1056-1065.
  2. A. Turabekova, B. F. Rasulev, QSAR Analysis of the Structure—Toxicity Relationship of Aconitum and Delphinium Diterpene Alkaloids, Chemistry of Natural Compounds, 2005, 41(2), 213-219
  3. Tulyasheva M., Rasulev B., Tojiboev A.G., Turgunov K.K., Tashkhodjaev B., Abdullaev N.D., Shakhidoyatov K.M., Synthesis, tautomeric states and crystal structure of (Z)-ethyl 2-cyano-2-(3H-quinazoline-4-ylidene) acetate and (Z)-ethyl 2-cyano-2-(2-methyl-3H-quinazoline-4-ylidene) acetate, Molecules, 2005, 10(9), 1209-17


  1. Turabekova M., Rasulev B F., A QSAR Toxicity Study of a Series of Alkaloids with the Lycoctonine Skeleton, Molecules, 2004, 9, 1194-1207.
  2. Turabekova M., Rasulev B F., Levkovich, M.G., Aconitum and Delphinium sp. Alkaloids as Antagonist Modulators of Voltage-Gated Na Channels: PM3 and QSAR Investigations, BioChem Press, 2004, Nov 29 – Dec 12.


  1. Rasulev B.F., Levkovich M.G., Abdullaev N.D., Sham’yanov I.D., Analysis of the biological activity of some sesquiterpenoids by QSAR, Chemistry of Natural Compounds, 2000, p.3-5, special issue.


  1. Rasulev B.F., Kobilov M.N., Saidhodjaev A.I., Study of spatial structure of elemans renardine and renardinine by two-dimensional NMR spectroscopy methods, Chemistry of Natural Compounds, 1998, special issue.


  1. Rasulev B.F., Levkovich M.G., Abdullaev N.D., Investigation of the spatial structure of the sesquiterpene lactone hanphyllin by 1H NMR spectroscopy using the shift reagent Eu(FOD)3, Chemistry of Natural Compounds, 1995, 31, 196-199.


  1. Patent “Development of new cleaning agent”, (RU № 6046 dated 29.07.1999), Republic of Uzbekistan


  1. Rasulev B., Lončarić Božić A., Dionysiou D.D., Kušić H. (2020) Modeling of Photooxidative Degradation of Aromatics in Water Matrix: A Quantitative Structure−Property Relationship Approach, In: Computational Photocatalysis: Modeling of Photophysics and Photochemistry at Interfaces, Eds. Kilin D., Kilina S., Han Y. ACS Symposium Series, Chapter 12, pp.257-292
  2. Rasulev B. (2020) Ecotoxicological QSAR Modeling of Nanomaterials: Methods in 3D-QSARs and Combined Docking Studies for Carbon Nanostructures. In: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY


  1. Wyrzykowska E., Jagiello K., Rasulev B., Puzyn T. (2019) Descriptors in Nano-QSAR/QSPR Modeling, In: Computational Nanotoxicology: Challenges and Perspectives, eds. Gajewicz A., Puzyn T., CRC Press, pp. 552


  1. Toropov, A.A., Toropova, A.P., Benfenati, E., Nicolotti, O., Carotti, A., Nesmerak, K., Veselinović, A.M., Veselinović, J.B., Duchowicz, P.R., Bacelo, D. and Castro, E.A., Rasulev B., Leszczynska D., Leszczynski D. (2017). QSPR/QSAR analyses by means of the CORAL software: results, challenges, perspectives. In Pharmaceutical Sciences: Breakthroughs in Research and Practice, IGI Global, USA, pp. 929-955.


  1. Rasulev, B. (2016) “Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures”, In: Handbook of Computational Chemistry, Eds. Jerzy Leszczynski (Jackson State University, USA) and Tomasz Puzyn (University of Gdansk, Poland), Springer, Netherlands


  1. Majumdar D., Roszak S., Wang J., Dinadayalane T.C., Rasulev B., Pinto H., Leszczynski J. (2014) “Advances in In Silico Research on Nerve Agents”, In:  Practical Aspects of Computational Chemistry III, Ed. Leszczynski J., Chapter 10, Springer US, pp.283-322


  1. Rasulev B., Gajewicz,A., Puzyn T., Leszczynska D., Leszczynski J. (2012) “Nano-QSAR: Advances and Challenges”, In: Towards Efficient Designing of Safe Nanomaterials,  Eds: Jerzy Leszczynski (Jackson State University, USA) and Tomasz Puzyn (University of Gdansk, Poland),  Chapter 10, Royal Society of Chemistry, UK, ISBN 9781849734530 (This book is the first to provide a comprehensive review of recent progress and challenges in the risk assessment of nanomaterials by empirical and computational techniques).


  1. Rasulev B., Leszczynska D., Leszczynski J. (2011) “Nanoparticles: Towards Predicting Their Toxicity and Physico-Chemical Properties”, In: Advanced Methods and Applications in Chemoinformatics: Research Progress and New Applications, Eds. Castro E.A., Haghi  A.K., Chapter 3, IGI Global, 2011, 92-110


  1. Toropov A.A., Rasulev B.F., Leszczynska D. and Leszczynski J. (2008) “New Approach to QSPR Modeling of Fullerene C60 Solubility in Organic Solvents: An Application of SMILES-Based Optimal Descriptors”, In: Medicinal Chemistry and Pharmacological Potential of Fullerenes and Carbon Nanotubes, Eds. F. Cataldo, T. Da Ros, Chapter 14, Springer Science + Business Media B.V.