Combining Group-Contribution concept and graph neural networks toward interpretable molecular property models
ARN Aouichaoui, F Fan, SS Mansouri… - Journal of Chemical …, 2023 - ACS Publications
Quantitative structure–property relationships (QSPRs) are important tools to facilitate and
accelerate the discovery of compounds with desired properties. While many QSPRs have …
accelerate the discovery of compounds with desired properties. While many QSPRs have …
Novel solubility prediction models: Molecular fingerprints and physicochemical features vs graph convolutional neural networks
Predicting both accurate and reliable solubility values has long been a crucial but
challenging task. In this work, surrogated model-based methods were developed to …
challenging task. In this work, surrogated model-based methods were developed to …
Experimental and computational prediction of glass transition temperature of drugs
A Alzghoul, A Alhalaweh, D Mahlin… - Journal of chemical …, 2014 - ACS Publications
Glass transition temperature (T g) is an important inherent property of an amorphous solid
material which is usually determined experimentally. In this study, the relation between T g …
material which is usually determined experimentally. In this study, the relation between T g …
[HTML][HTML] Predicting entropy and heat capacity of hydrocarbons using machine learning
Chemical substances are essential in all aspects of human life, and understanding their
properties is essential for developing chemical systems. The properties of chemical species …
properties is essential for developing chemical systems. The properties of chemical species …
Accurate predictions of aqueous solubility of drug molecules via the multilevel graph convolutional network (MGCN) and SchNet architectures
Deep learning based methods have been widely applied to predict various kinds of
molecular properties in the pharmaceutical industry with increasingly more success. In this …
molecular properties in the pharmaceutical industry with increasingly more success. In this …
QSPR flash point prediction of solvents using topological indices for application in computer aided molecular design
Incorporating consideration for safety issues while selecting solvents for processes has
become crucial in light of the chemical process accidents involving solvents that have taken …
become crucial in light of the chemical process accidents involving solvents that have taken …
Polymer genome: a data-powered polymer informatics platform for property predictions
The recent successes of the Materials Genome Initiative have opened up new opportunities
for data-centric informatics approaches in several subfields of materials research, including …
for data-centric informatics approaches in several subfields of materials research, including …
QSPR prediction of flash point of esters by means of GFA and ANFIS
A Khajeh, H Modarress - Journal of hazardous materials, 2010 - Elsevier
A quantitative structure property relationship (QSPR) study was performed to develop a
model for prediction of flash point of esters based on a diverse set of 95 components. The …
model for prediction of flash point of esters based on a diverse set of 95 components. The …
A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties
Environmental properties of compounds provide significant information in treating organic
pollutants, which drives the chemical process and environmental science toward eco …
pollutants, which drives the chemical process and environmental science toward eco …
Prediction of critical properties and boiling point of fluorine/chlorine-containing refrigerants
In this work, molecular groups were used as the descriptor of molecular structures,
combining with multi-layer perceptron algorithm to establish the prediction models of boiling …
combining with multi-layer perceptron algorithm to establish the prediction models of boiling …