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SAR and QSAR in Environmental Research Volume 31, 2020 - Issue 6
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Research Article

Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis

K. Ghosha Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. H. S. Gour University, Sagar, IndiaView further author information
,
B. Bhardwaja Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. H. S. Gour University, Sagar, IndiaView further author information
,
S.A. Aminb Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, IndiaORCID Iconhttps://orcid.org/0000-0003-4799-7322View further author information
ORCID Icon,
T. Jhab Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, IndiaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-9996-738XView further author information
ORCID Icon &
S. Gayena Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. H. S. Gour University, Sagar, IndiaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-3367-578XView further author information
ORCID Icon
Pages 439-455 | Received 13 Mar 2020, Accepted 17 May 2020, Published online: 15 Jun 2020

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Sourav Sardar, Jyotisha, Sk. Abdul Amin, Samima Khatun, Insaf Ahmed Qureshi, Umesh Kumar Patil, Tarun Jha & Shovanlal Gayen. (2023) Identification of structural fingerprints among natural inhibitors of HDAC1 to accelerate nature-inspired drug discovery in cancer epigenetics. Journal of Biomolecular Structure and Dynamics 0:0, pages 1-15.
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S. Das, S.A. Amin, S. Gayen & T. Jha. (2022) Insight into the structural requirements of gelatinases (MMP-2 and MMP-9) inhibitors by multiple validated molecular modelling approaches: Part II. SAR and QSAR in Environmental Research 33:3, pages 167-192.
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S. Ahmadi, S. Lotfi, S. Afshari, P. Kumar & E. Ghasemi. (2021) CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors. SAR and QSAR in Environmental Research 32:12, pages 1013-1031.
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S. Nandi, P. Kumar, S.A. Amin, T. Jha & S. Gayen. (2021) First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis. SAR and QSAR in Environmental Research 32:11, pages 917-939.
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Articles from other publishers (5)

Balint Dudas & Maria A. Miteva. (2023) Computational and artificial intelligence-based approaches for drug metabolism and transport prediction. Trends in Pharmacological Sciences.
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Faezeh Tajiani, Shahin Ahmadi, Shahram Lotfi, Parvin Kumar & Ali Almasirad. (2023) In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization. BMC Chemistry 17:1.
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Md Moinul, Sk Abdul Amin, Tarun Jha & Shovanlal Gayen. (2022) Updated chemical scaffolds of ABCG2 inhibitors and their structure-inhibition relationships for future development. European Journal of Medicinal Chemistry 241, pages 114628.
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Priyanka De, Supratik Kar, Pravin Ambure & Kunal Roy. (2022) Prediction reliability of QSAR models: an overview of various validation tools. Archives of Toxicology 96:5, pages 1279-1295.
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Victor Kuz’min, Anatoly Artemenko, Luidmyla Ognichenko, Alexander Hromov, Anna Kosinskaya, Sergij Stelmakh, Zoe L. Sessions & Eugene N. Muratov. (2021) Simplex representation of molecular structure as universal QSAR/QSPR tool. Structural Chemistry 32:4, pages 1365-1392.
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