The molecular structures of 77 nitroaromatic compounds have been correlated to their thermal stabilities by combining the quantitative structure-property relationship (QSPR) method with density functional theory (DFT). More than 300 descriptors (constitutional, topological, geometrical and quantum chemical) have been calculated, and multilinear regressions have been performed to find accurate quantitative relationships with experimental heats of decomposition (-ΔH). In particular, this work demonstrates the importance of accounting for chemical mechanisms during the selection of an adequate experimental data set. A reliable QSPR model that presents a strong correlation with experimental data for both the training and the validation molecular sets (R (2) = 0.90 and 0.84, respectively) was developed for non-ortho-substituted nitroaromatic compounds. Moreover, its applicability domain was determined, and the model's predictivity reached 0.86 within this applicability domain. To our knowledge, this work has produced the first QSPR model, developed according to the OECD principles of regulatory acceptability, for predicting the thermal stabilities of energetic compounds.
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http://dx.doi.org/10.1007/s00894-010-0908-0 | DOI Listing |
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 PDFJ Mol Model
December 2024
Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics, and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA.
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 PDFMol 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 PDFHeliyon
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.
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