A quantitative structure-property relationship (QSPR) model is developed to predict the logarithm of the soil sorption coefficient of 82 organic compounds. The data set contains polar and nonpolar, saturated, unsaturated, aliphatic, aromatic, and polycyclic aromatic compounds covering a log K(oc) range from about 1 to 6 log units. The best correlation equation, containing only five constitutional descriptors (number of benzene rings, molecular weight, number of N, O, and S atoms), predicts log K(oc) with a squared correlation coefficient of 0.94, having a standard deviation, s, of 0.33. The model is validated with an external set of 43 compounds not included in the training set. The descriptors involved in the model can be obtained easily from the molecular formula without any further calculation; therefore, the model is ready to use by environmental scientists with no background in quantum chemistry or chemical graph theory or when no software is available.
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http://dx.doi.org/10.1021/ci0341666 | DOI Listing |
Heliyon
January 2025
Department of Mathematics, Faculty of Sciences, Ghazi University, Dera Ghazi Khan, 32200, Pakistan.
Chemical structures may be defined based on their topology, which allows for the organization of molecules and the representation of new structures with specific properties. We use topological indices, which are precise numerical measurements independent of structure, to measure the bonding arrangement of a chemical network. An essential objective of studying topological indices is to collect and alter chemical structure data to develop a mathematical relationship between structures and physico-chemical properties, bio-activities, and associated experimental factors.
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January 2025
Department of Mathematical Sciences, Faculty of Science, Somali National University, Mogadishu Campus, Mogadishu, Somalia.
In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy.
View Article and Find Full Text PDFMol Inform
January 2025
Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.
Recent advances in machine learning have significantly impacted molecular design, notably the molecular generation method combining the chemical variational autoencoder (VAE) with Gaussian mixture regression (GMR). In this method, a mathematical model is constructed with X as the latent variable of the molecule and Y as the target properties and activities. Through direct inverse analysis of this model, it is possible to generate molecules with the desired target properties.
View Article and Find Full Text PDFEnviron Pollut
January 2025
Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Despite the significant benefits of aquatic passive sampling (low detection limits and time-weighted average concentrations), the use of passive samplers is impeded by uncertainties, particularly concerning the accuracy of sampling rates. This study employed a systematic evaluation approach based on the combination of meta-analysis and quantitative structure-property relationships (QSPR) models to address these issues. A comprehensive meta-analysis based on extensive data from 298 studies on the Polar Organic Chemical Integrative Sampler (POCIS) identified essential configuration parameters, including the receiving phase (type, mass) and the diffusion-limiting membrane (type, thickness, pore size), as key factors influencing uptake kinetic parameters.
View Article and Find Full Text PDFMAbs
December 2025
Department of Purification, Microbiology and Virology, Genentech Inc, South San Francisco, CA, USA.
In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation. However, the work required for upstream protein expression and downstream purification development often conflicts with timeline pressures and material constraints, limiting the number of molecules and process conditions that can reasonably be assessed. Recently, high-throughput batch-binding screen data along with improved molecular descriptors have enabled development of robust quantitative structure-property relationship (QSPR) models that predict monoclonal antibody chromatographic binding behavior from the amino acid sequence.
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