B. Sepehri

发表

A review on created QSPR models for predicting ionic liquids properties and their reliability from chemometric point of view

B. Sepehri, 2020 .

QSPR/QSAR solely based on molecular surface electrostatic potentials for benzenoid hydrocarbons

B. Sepehri, R. Ghavami, 2016, Journal of the Iranian Chemical Society.

Molecular docking and QSAR analysis of naphthyridone derivatives as ATAD2 bromodomain inhibitors: application of CoMFA, LS-SVM, and RBF neural network

B. Sepehri, R. Ghavami, Zolaikha Rasouli, 2016, Medicinal Chemistry Research.

High predictive QSAR models for predicting the SARS coronavirus main protease inhibition activity of ketone-based covalent inhibitors

B. Sepehri, R. Ghavami, Mohammad Kohnehpoushi, 2021, Journal of the Iranian Chemical Society.

Design of new CD38 inhibitors based on CoMFA modelling and molecular docking analysis of 4‑amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides

B. Sepehri, R. Ghavami, 2018, SAR and QSAR in environmental research.

Investigation of retention behavior of polychlorinated biphenyl congeners on 18 different HRGC columns using molecular surface average local ionization energy descriptors.

B. Sepehri, R. Ghavami, 2012, Journal of chromatography. A.

Development linear and non-linear QSAR models for predicting AXL kinase inhibitory activity of N-[4-(quinolin-4-yloxy)phenyl]benzenesulfonamides

B. Sepehri, R. Ghavami, R. Ahmadi, 2019, Journal of receptor and signal transduction research.

The in silico identification of potent anti-cancer agents by targeting the ATP binding site of the N-domain of HSP90

B. Sepehri, R. Ghavami, M. Rezaei, 2018, SAR and QSAR in environmental research.

Identifying SARS-CoV-2 main protease inhibitors by applying the computer screening of a large database of molecules

B. Sepehri, R. Ghavami, R. Ahmadi, 2022, SAR and QSAR in environmental research.

The identification of new ATAD2 bromodomain inhibitors: the application of combined ligand and structure-based virtual screening

B. Sepehri, R. Ghavami, 2017, SAR and QSAR in environmental research.

Molecular docking and CoMFA studies of thiazoloquin(az)olin(on)es as CD38 inhibitors: determination of inhibitory mechanism, pharmacophore interactions, and design of new inhibitors

B. Sepehri, R. Ghavami, 2017, Journal of biomolecular structure & dynamics.

Design new P-glycoprotein modulators based on molecular docking and CoMFA study of α, β-unsaturated carbonyl-based compounds and oxime analogs as anticancer agents

B. Sepehri, R. Ghavami, 2017 .

Towards the In-silico Design of New HSP90 Inhibitors: Molecular Docking and 3D-QSAR CoMFA Studies of Tetrahydropyrido [4, 3-d] Pyrimidine Derivatives as HSP90 Inhibitors.

B. Sepehri, R. Ghavami, 2018, Medicinal chemistry (Shariqah (United Arab Emirates)).

Robust and predictive QSAR models for predicting the D2, 5-HT1A, and 5-HT2A inhibition activities of fused tricyclic heterocycle piperazine (piperidine) derivatives as atypical antipsychotic drugs

B. Sepehri, R. Ghavami, R. Ahmadi, 2022, Journal of Molecular Structure.

Machine learning-based quantitative structure–retention relationship models for predicting the retention indices of volatile organic pollutants

B. Sepehri, R. Ghavami, R. Ahmadi, 2021, International Journal of Environmental Science and Technology.

Design of new CD38 inhibitors based on CoMFA modelling and molecular docking analysis of 4‑amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides

B. Sepehri, R. Ghavami, 2018, SAR and QSAR in environmental research (Print).

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection.

Bakhtyar Sepehri, Nematollah Omidikia, Mohsen Kompany-Zareh, 2018, Combinatorial chemistry & high throughput screening.