Results 21 to 30 of about 2,595 (161)

Study of Factors Influencing High-Temperature Detergency of Alkylated Phenyl Salicylates

Tribology Online, 2008
Lubricants with longer drain interval should be easy to maintain, economical, and environment friendly. Compact and high-performance machines require high-performance lubricants.
Hiroaki Koshima, Toshiyuki Tsubouchi, Yoshio Hisaeda   +2 more
doaj   +1 more source

Quantitative relationships for the prediction of the vapor pressure of some hydrocarbons from the van der Waals molecular surface [PDF]

Journal of the Serbian Chemical Society, 2015
A quantitative structure - property relationship (QSPR) modeling of vapor pressure at 298.15 K, expressed as log (VP / Pa) was performed for a series of 84 hydrocarbons (63 alkanes and 21 cycloalkanes) using the van der Waals (vdW) surface area,
Olariu Tudor, Vlaia Vicenţiu, Ciubotariu Ciprian, Dragoş Dan, Ciubotariu Dan, Mracec Mircea   +5 more
doaj   +1 more source

Development of remediation technologies for organic contaminants informed by QSAR/QSPR models

Environmental Advances, 2021
Release of persistent organic pollutants (POPs) into the environmental media causes serious environmental and health implications. Experimental studies along with quantitative structure activity/property relationship (QSAR/QSPR) models are used to ...
Aryan Samadi, Azadeh Kermanshahi Pour, Rob Jamieson   +2 more
doaj   +1 more source

QSPR Study on the permeability of drugs across Caco-2 monolayer(药物对Caco-2单细胞层穿透性的QSPR研究)

Zhejiang Daxue xuebao. Lixue ban, 2009
Caco-2单细胞层用于模拟或预测体内小肠吸收均较剥离的小肠膜效果好.利用偏最小二乘分析方法研究Vol Surf参数与药物透过Caco-2单细胞层的渗透系数之间的定量结构-性质关系(QSPR),得到较好的结果(r2 = 0.95,q2=0.75).对其它类药物的预测结果表明,用结构不同的化合物所建立的模型对肽类药物以及同系物分子均有一定的预测能力,但是当分子的柔性键多,可能形成分子内氢键时,模型的预测能力大大下降.参数分析表明对Caco-2单细胞层渗透性高的药物分子必须具有合适的氢键给体和受体 ...
HUGui-xiang(胡桂香), ZHANGChang-hui(张长会), ZHAOWen-na(赵文娜), YUQing-sen(俞庆森)   +3 more
doaj   +1 more source

A QSPR analysis of physical properties of antituberculosis drugs using neighbourhood degree-based topological indices and support vector regression

Heliyon
Topological indices are molecular descriptors used in QSPR modelling to predict the physicochemical properties of molecules. Topological indices are used in numerous applications in drug design.
Muhammad Shafii Abubakar, Kazeem Olalekan Aremu, Maggie Aphane, Lateef Babatunde Amusa   +3 more
doaj   +1 more source

Ranking-based selection of non-linear quantitative structure-property relationship models for prediction of bioconcentration factor of triazine derivatives as pesticide candidates [PDF]

Acta Periodica Technologica
The estimation of ecotoxicity and bioaccumulation of compounds as pesticide candidates is an important step in the estimation of their potential practical use.
Kovačević Strahinja, Karadžić-Banjac Milica, Podunavac-Kuzmanović Sanja, Jevrić Lidija   +3 more
doaj   +1 more source

Machine Learning for Green Solvents: Assessment, Selection and Substitution

Advanced Science, EarlyView.
Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta, Janhavi Nistane, Abhishek Sose, Harikrishna Sahu, Rampi Ramprasad   +4 more
wiley   +1 more source

Unsupervised Hierarchical Symbolic Regression for Interpretable Property Modeling in Complex Multi‐Variable Systems

Advanced Science, EarlyView.
UHSR translates complex chemical behavior into clear and explainable equations. Applied to thin‐layer chromatography, it automatically uncovers the mathematical rules linking a molecule's structure to its polarity. This approach matches the accuracy of advanced AI while providing interpretable results, earning greater trust from chemists. The method is
Siyu Lou, Chengchun Liu, Dongxiao Zhang, Yuntian Chen, Fanyang Mo   +4 more
wiley   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source