The fish-tail temperatures denoted as T* have been determined or collected for 85 ternary systems based on three tetraethylene glycol monoalkyl ethers C(i)E(4) (i = 6, 8, 10), water, and 43 hydrocarbon oils of various hydrophobicities. Fourteen fragrant mono- and sesquiterpenes in addition to 29 model oils, including n-alkanes, cyclohexenes, cyclohexanes, and alkylbenzenes, were investigated in order to establish a QSPR model for the prediction of T* as a function of the chemical structure of the oils. Only two molecular descriptors related to branching and molecular size (Kier A3) and polarizability (average negative softness) of the molecules are necessary to model and predict the values of T* and EACN (equivalent alkane carbon number) of unsaturated and/or cyclic and/or branched hydrocarbons exhibiting an EACN ranging from -4 and +35. Results are discussed in terms of evolution of the effective packing parameter of the surfactants according to temperature and oil penetration into the interfacial film.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/la904836m | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Molecular insights on the solvent screening for the benzene extraction from fuels using ionic liquids via QSPR method.
Sci Rep
December 2024
School of Chemical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
Benzene separation from hydrocarbon mixtures is a challenge in the refining and petrochemical industries. The application of liquid-liquid extraction process using ionic liquids (I.Ls) is an option for this separation.
View Article and Find Full Text PDFChemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure-property relationship modeling.
J Mol Model
December 2024
Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics, and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA.
Article Synopsis
- Boron-dipyrromethene (BODIPY) compounds have valuable optical properties and are used in various fields like imaging and electronics; to enhance their design, understanding the link between their structures and optical properties is essential.
- A machine learning-based model was developed, showing high predictive accuracy for the BODIPY compounds' maximum absorption wavelength, proving its effectiveness through strong correlation coefficients.
- The study used computational chemistry methods to optimize BODIPY structures and employed machine learning techniques to analyze 131 compounds, highlighting the significance of molecular characteristics like branching and specific functional groups in determining their properties.
Human-in-the-loop active learning for goal-oriented molecule generation.
J Cheminform
December 2024
Department of Computer Science, Aalto University, 02150, Espoo, Finland.
Machine learning (ML) systems have enabled the modelling of quantitative structure-property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug discovery by guiding generative artificial intelligence (AI) agents to explore desired chemical spaces. However, they often struggle to generalize due to the limited scope of the training data.
View Article and Find Full Text PDFFormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.
Mol Pharm
December 2024
Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macau 999078, China.
The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubility and permeability, still requires improvement, limiting high-throughput applications of BCS, which is essential for evaluating drug candidate developability and guiding formulation decisions in the early stages of drug development. In recent years, advancements in machine learning (ML) and molecular characterization have revealed the potential of quantitative structure-performance relationships (QSPR) for rapid and accurate BCS classification.
View Article and Find Full Text PDFBicubic regression analysis a novel approach for estimation of physicochemical properties of skin cancer drugs through degree based entropy.
Heliyon
October 2024
Department of Mathematics, Faculty of Sciences, Ghazi University, Dera Ghazi Khan, 32200, Pakistan.
Skin cancer poses a significant risk to the healthcare system worldwide and is projected to increase substantially over the next two decades, particularly if not detected in its early stages. The primary aim of this study is to construct a quantitative structure-property relationship (QSPR) by correlating calculated entropies with topological indices and specific physical-chemical properties of pharmaceuticals, in order to enhance their usefulness. The bicubic regression model is constructed through degree-based topological entropies to perform the QSPR analysis for the prediction of physiochemical properties like polar surface area, complexity, molar refractivity, boiling point, and polarity of skin cancer drugs.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!