Applications of Chemometric Methods to Elucidate Physicochemical Requirements for Binding of PTP1B Inhibitors to Its Target
Authors
Abstract
The quantitative structure activity relationship (QSAR) models were developed using multiple linear regression (MLR), partial least square (PLS) and feed forward neural network (FFNN) for a set of 49 PTP1B inhibitors of diabetes. The MLR,PLS and FFNN generated analogous models with good prognostic ability and all the other statistical values, such as r, r2, r2cv and F and S values, remained satisfactory. The results obtained from this study indicate the importance of dipole moment Y component, Number of H- bond and VAMP polarization (whole molecule) in determining the inhibitory activity of PTP1B inhibitor. The best artificial neural network model is a fullyconnected, feed forward back propagation network with a 2-5-1 architecture. This statistics is appropriate to the further design of novel PTP1B receptor. The similarity (CARBO and HODGKIN) analysis was also done on the same series which positively support the previous results. The QSAR study reported in the present study provide important structural situation, related to antidiabetic activity. Present study enlightens the path of determining the potent lead compounds of PTP1B antagonist.
Highlights
Copyright
© 2018, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.
Similar Articles
Quantitative Structure–Activity Relationship Analysis of Thiophene Derivatives to Explore the Structural Requirements for c-Jun NH 2 -Terminal Kinase 1 Inhibitory Activity
Nagpal A, Chauhan M. Quantitative Structure–Activity Relationship Analysis of Thiophene Derivatives to Explore the Structural Requirements for c-Jun NH 2 -Terminal Kinase 1 Inhibitory Activity. J Rep Pharm Sci. 2019;8(2):e147320. doi: https://doi.org/10.4103/jrptps.jrptps_32_18
Molecular Docking and QSAR Study of 2-Benzoxazolinone, Quinazoline and Diazocoumarin Derivatives as Anti-HIV-1 Agents
Faghihi K, Safakish M, Zebardast T, Hajimahdi Z, Zarghi A. Molecular Docking and QSAR Study of 2-Benzoxazolinone, Quinazoline and Diazocoumarin Derivatives as Anti-HIV-1 Agents. Iran J Pharm Res. 2019;18(3):e126172. doi: https://doi.org/10.22037/ijpr.2019.1100746
QSAR Study on Anti-HIV-1 Activity of 4-Oxo-1,4-dihydroquinoline and 4-Oxo-4H-pyrido[1,2-a]pyrimidine Derivatives Using SW-MLR, Artificial Neural Network and Filtering Methods
Hajimahdi Z, Ranjbar A, Abolfazl Suratgar A, Zarghi A. QSAR Study on Anti-HIV-1 Activity of 4-Oxo-1,4-dihydroquinoline and 4-Oxo-4H-pyrido[1,2-a]pyrimidine Derivatives Using SW-MLR, Artificial Neural Network and Filtering Methods. Iran J Pharm Res. 2015;14(Suppl):e125249. doi: https://doi.org/10.22037/ijpr.2015.1714
2D-QSAR and docking studies of 4-anilinoquinazoline derivatives as epidermal growth factor receptor tyrosine kinase inhibitors
Ghasemi Dogaheh M, Ebrahimi H, Yousefbeyk F, ghasemi S. 2D-QSAR and docking studies of 4-anilinoquinazoline derivatives as epidermal growth factor receptor tyrosine kinase inhibitors. koomesh. 2022;24(3):e152750. doi:
A Computational Study of Some Benzenesulfonamide Derivatives as HIV- 1 Protease Enzyme Inhibitors: Three-Dimensional Quantitative Structure-Activity Relationship and Molecular Docking
Abdizade R, Abdizadeh T. A Computational Study of Some Benzenesulfonamide Derivatives as HIV- 1 Protease Enzyme Inhibitors: Three-Dimensional Quantitative Structure-Activity Relationship and Molecular Docking. koomesh. 2025;27(6):e159682. doi: https://doi.org/10.69107/koomesh-159682
- Scopus by DOI: 0
Last Update: 2 weeks ago
- Scopus by Title: 0
Last Update: 2 weeks ago
- Scopus by Title (Ref): 0
Last Update: 2 weeks ago
- CrossRef: 0
Last Update: 2 days ago