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A Two-Step Protocol for Umpolung Functionalization of Ketones Via Enolonium Species
Published on: August 16, 2018
ETM and ANN study for polysubstituted 2H-pyran-2-ones
Murat Saracoglu1, Murat Alper Basaran, Pallavi Thul
1Erciyes University, Faculty of Education, 38039 Kayseri, Turkey. muratsaracoglu@gmail.com
Medicinal Chemistry (Shariqah (United Arab Emirates))
|October 25, 2012
Summary
This study investigated new hepatoprotective agents, 2H-pyran-2-ones polysubstitutes, using the Electronic-Topological Method. Artificial Neural Networks (ANNs) proved superior to Partial Least Squares Regression for modeling their enzyme activity relationships.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Biochemistry
Background:
- Hepatoprotective agents are crucial for managing liver diseases.
- 2H-pyran-2-ones represent a novel class of potential hepatoprotective compounds.
- Understanding structure-activity relationships is key to developing effective drugs.
Purpose of the Study:
- To investigate the structure-activity relationships of 2H-pyran-2-ones polysubstitutes as hepatoprotective agents.
- To explore the correlation between molecular descriptors and enzyme activities (GPT, GOT, ACP, ALP, GlDH).
- To compare the efficacy of different statistical modeling techniques.
Main Methods:
- Utilized the Electronic-Topological Method (ETM) to identify active molecular fragments.
- Calculated various Quantitative Structure-Activity Relationship (QSAR) descriptors.
- Employed Partial Least Squares Regression (PLS1, PLS2) and Artificial Neural Networks (ANNs) for data analysis.
Main Results:
- ETM identified key molecular fragments associated with hepatoprotective activity.
- QSAR descriptors were calculated, including molecular weight, electronic properties, and dipole moment.
- ANN models demonstrated superior performance in modeling and validation compared to PLS methods, handling nonlinearity and multicollinearity effectively.
Conclusions:
- 2H-pyran-2-ones polysubstitutes show promise as hepatoprotective agents.
- The study highlights the effectiveness of ANNs in QSAR analysis for complex biological data.
- This research provides a foundation for the rational design of novel hepatoprotective drugs.
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