Molecular docking and 3D-QSAR-based virtual screening of flavonoids as potential aromatase inhibitors against estrogen-dependent breast cancer

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

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Discovery of Camptothecin Based Topoisomerase I Inhibitors: Identification Using an Atom Based 3D-QSAR, Pharmacophore Modeling, Virtual Screening and Molecular Docking Approach

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207319666160810154346 ◽

2016 ◽

Vol 19 (9) ◽

pp. 752-763 ◽

Cited By ~ 4

Author(s):

Sanal Dev ◽

Sunil Dhaneshwar ◽

Bijo Mathew

Keyword(s):

Molecular Docking ◽

Virtual Screening ◽

Topoisomerase I ◽

3D Qsar ◽

Pharmacophore Modeling ◽

Topoisomerase I Inhibitors ◽

Docking Approach

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Toward the Identification of Novel Carbonic Anhydrase XIV Inhibitors using 3D-QSAR Pharmacophore Model, Virtual Screening and Molecular Docking Study

Letters in Drug Design & Discovery ◽

10.2174/15701808113106660083 ◽

2014 ◽

Vol 11 (4) ◽

pp. 403-412 ◽

Cited By ~ 1

Author(s):

Tao Liu ◽

Lu Zhou ◽

Taijin Wang ◽

Lufen He ◽

Xiangyang Tang

Keyword(s):

Molecular Docking ◽

Virtual Screening ◽

Carbonic Anhydrase ◽

Pharmacophore Model ◽

3D Qsar ◽

Docking Study ◽

Molecular Docking Study

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Pharmacophore modeling, virtual screening, and 3D-QSAR studies on a series of non-steroidal aromatase inhibitors

Medicinal Chemistry Research ◽

10.1007/s00044-014-1257-9 ◽

2014 ◽

Vol 24 (5) ◽

pp. 1901-1915 ◽

Cited By ~ 5

Author(s):

Huiding Xie ◽

Kaixiong Qiu ◽

Xiaoguang Xie

Keyword(s):

Virtual Screening ◽

Aromatase Inhibitors ◽

3D Qsar ◽

Pharmacophore Modeling ◽

Qsar Studies ◽

Steroidal Aromatase Inhibitors

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Molecular Modeling Studies on 2,4-Disubstituted Imidazopyridines as Anti-Malarials: Atom-Based 3D-QSAR, Molecular Docking, Virtual Screening, In-Silico ADMET and Theoretical Analysis

Journal of Computational Biophysics and Chemistry ◽

10.1142/s2737416521500125 ◽

2021 ◽

pp. 2150012

Author(s):

Suraj N. Mali ◽

Anima Pandey

Keyword(s):

Molecular Docking ◽

Molecular Modeling ◽

Virtual Screening ◽

In Silico ◽

Energy Gap ◽

3D Qsar ◽

Modeling Studies ◽

In Vitro Investigation ◽

In Silico Admet ◽

Molecular Modeling Studies

Malarial parasites have been reported for moderate-high resistance towards classical antimalarial agents and henceforth development of newer novel chemical entities targeting multiple targets rather than targeting single target will be a highly promising strategy in antimalarial drug discovery. Herein, we carried out molecular modeling studies on 2,4-disubstituted imidazopyridines as anti-hemozoin formation inhibitors by using Schrödinger’s molecular modeling package (2020_4). We have developed statistically robust atom-based 3D-QSAR model (training set, [Formula: see text]; test set, [Formula: see text]; [Formula: see text] [Formula: see text]; root-mean-square error, [Formula: see text]; standard deviation, [Formula: see text]). Our molecular docking, in-silico ADMET analysis showed that dataset molecule 37, has highly promising results. Our ligand-based virtual screening resulted in top five ZINC hits, among them ZINC73737443 hit was observed with lesser energy gap, i.e. 7.85[Formula: see text]eV, higher softness value (0.127[Formula: see text]eV), and comparatively good docking score of [Formula: see text]10.2[Formula: see text]kcal/mol. Our in-silico analysis for a proposed hit, ZINC73737443 showed that this molecule has good ADMET, in-silico nonames toxic as well as noncarcinogenic profile. We believe that further experimental as well as the in-vitro investigation will throw more lights on the identification of ZINC73737443 as a potential antimalarial agent.

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