Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review
Mathematical modeling and simulation methods are important tools in studying various
processes in science and engineering. In the current review, we focus on the applications of …
processes in science and engineering. In the current review, we focus on the applications of …
Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks
The past two decades are regarded as the golden age of using neural networks (NNs) in
chemoinformatics. However, two major issues have arisen concerning their use: redundancy …
chemoinformatics. However, two major issues have arisen concerning their use: redundancy …
OPERA models for predicting physicochemical properties and environmental fate endpoints
The collection of chemical structure information and associated experimental data for
quantitative structure–activity/property relationship (QSAR/QSPR) modeling is facilitated by …
quantitative structure–activity/property relationship (QSAR/QSPR) modeling is facilitated by …
CATMoS: collaborative acute toxicity modeling suite
K Mansouri, AL Karmaus, J Fitzpatrick… - Environmental …, 2021 - ehp.niehs.nih.gov
Background: Humans are exposed to tens of thousands of chemical substances that need to
be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for …
be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for …
CoMPARA: collaborative modeling project for androgen receptor activity
Background: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the
interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The …
interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The …
Open-source QSAR models for pKa prediction using multiple machine learning approaches
Background The logarithmic acid dissociation constant pKa reflects the ionization of a
chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the …
chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the …
[HTML][HTML] Integration of handheld NIR and machine learning to “Measure & Monitor” chicken meat authenticity
By combining portable, handheld near-infrared (NIR) spectroscopy with state-of-the-art
classification algorithms, we developed a powerful method to test chicken meat authenticity …
classification algorithms, we developed a powerful method to test chicken meat authenticity …
Rapid and practical qualitative and quantitative evaluation of non-fumigated ginger and sulfur-fumigated ginger via Fourier-transform infrared spectroscopy and …
H Yan, PH Li, GS Zhou, YJ Wang, BH Bao, QN Wu… - Food Chemistry, 2021 - Elsevier
A strategy was developed to distinguish and quantitate nonfumigated ginger (NS-ginger)
and sulfur-fumigated ginger (S-ginger), based on Fourier transform near infrared …
and sulfur-fumigated ginger (S-ginger), based on Fourier transform near infrared …
A MATLAB toolbox for Self Organizing Maps and supervised neural network learning strategies
D Ballabio, M Vasighi - Chemometrics and intelligent laboratory systems, 2012 - Elsevier
Kohonen maps and Counterpropagation Neural Networks are two of the most popular
learning strategies based on Artificial Neural Networks. Kohonen Maps (or Self Organizing …
learning strategies based on Artificial Neural Networks. Kohonen Maps (or Self Organizing …
An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling
The increasing availability of large collections of chemical structures and associated
experimental data provides an opportunity to build robust QSAR models for applications in …
experimental data provides an opportunity to build robust QSAR models for applications in …