Results 31 to 40 of about 15,493,907 (286)

Predicting H2S solubility in ionic liquids by the quantitative structure-property relationship method using S sigma-profile molecular descriptors

, 2016
Predicting hydrogen sulfide (H2S) solubility in ionic liquids (ILs) is vital for industrial gas desulphurization. In this work, the qualitative analysis of the influence of cations and anions on the H2S solubility in ILs has been conducted. The results
Zhang, Xiangping, Zhao, Yongsheng, Gao, Jubao, Zhang, Suojiang, Huang, Ying, Afzal, Raja Muhammad   +5 more
core   +1 more source

Predicting Surface Tensions and Electrical Conductivities for High Molecular Weight Ionic Liquid Propellants Using Quantitative Structure-Property Relationships (QSPR) [PDF]

, 2023
Quantitative Structure-Property Relationships (QSPR) take in existing experimental property data and output predicted properties using statistical analysis and linear regressions.
Steven D., Chambreau, Debasis, Sengupta, David A., Newsome, Ghanshyam L., Vaghjiani, Mark J., Wolf   +4 more
core   +1 more source

Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship

Energy and AI
The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned.
Rodolfo S.M. Freitas, Xi Jiang
doaj   +1 more source

Artificial Neural Network and Support Vector Regression Applied in Quantitative Structure-property Relationship Modelling of Solubility of Solid Solutes in Supercritical CO2 [PDF]

Kemija u Industriji, 2020
In this study, the solubility of 145 solid solutes in supercritical CO2 (scCO2) was correlated using computational intelligence techniques based on Quantitative Structure-Property Relationship (QSPR) models.
Mohammed Moussaoui, Maamar Laidi, Salah Hanini, Mohamed Hentabli   +3 more
doaj   +1 more source

UNDERSTANDING QUANTITATIVE STRUCTURE–PROPERTY RELATIONSHIPS UNCERTAINTY IN ENVIRONMENTAL FATE MODELING [PDF]

Environmental Toxicology and Chemistry, 2013
Abstract In cases in which experimental data on chemical-specific input parameters are lacking, chemical regulations allow the use of alternatives to testing, such as in silico predictions based on quantitative structure–property relationships (QSPRs).
Sarfraz Iqbal, M., Golsteijn, L., Öberg, T., Sahlin, U., PAPA, ESTER, Kovarich, S., Huijbregts, M. A. J.   +6 more
openaire   +3 more sources

Minimum Detour Index of Tricyclic Graphs

Journal of Chemistry, 2019
The detour index of a connected graph is defined as the sum of the detour distances (lengths of longest paths) between unordered pairs of vertices of the graph.
Wei Fang, Zheng-Qun Cai, Xiao-Xin Li
doaj   +1 more source

Modelling stem cell differentiation related processes—A practical overview for biologists

FEBS Letters, EarlyView.
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar, Xinqi Yan, Lucas F. Jansen Klomp, Stefano Schivo, Janine N. Post   +4 more
wiley   +1 more source

Design and analysis strategies for robust microbiome ageing research

FEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik, İsmail Güderer, Yi Wang, Tayyaba Alvi, Prasoon Pandey, Handan Melike Dönertaş   +5 more
wiley   +1 more source

Machine-learning-driven QSPR models for energetic molecules: A review on safety and energetic properties prediction

Chemical Engineering Journal Advances
The performance prediction and rational design of energetic molecules (EMs) remain central challenges in their development. Traditional experimental methods are constrained by prohibitively high costs and inherent safety risks, highlighting the urgent ...
Mingchi Gao, Tengxin Huang, Mingtian Li, Yingjun Zhang, Liangliang Wang, Junjie Ding   +5 more
doaj   +1 more source

Artificial Intelligence in Drug Design

Molecules, 2018
Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area.
Gerhard Hessler, Karl-Heinz Baringhaus
doaj   +1 more source