In silicoprediction and screening of γ-secretase inhibitors by molecular descriptors and machine learning methods

2009 ◽  
pp. NA-NA ◽  
Author(s):  
Xue-Gang Yang ◽  
Wei Lv ◽  
Yu-Zong Chen ◽  
Ying Xue
Keyword(s):  
Machine Learning ◽  
Molecular Descriptors ◽  
Learning Methods ◽  
Machine Learning Methods

Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict?

2019 ◽  
Vol 19 (5) ◽  
pp. 362-372 ◽  
Author(s):  
Oleg A. Raevsky ◽  
Veniamin Y. Grigorev ◽  
Daniel E. Polianczyk ◽  
Olga E. Raevskaja ◽  
John C. Dearden
Keyword(s):  
Machine Learning ◽  
Quantum Chemical ◽  
Critical Analysis ◽  
Molecular Descriptors ◽  
Aqueous Solubility ◽  
Drug Solubility ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Intrinsic Solubility ◽  
Kinetic Solubility

Detailed critical analysis of publications devoted to QSPR of aqueous solubility is presented in the review with discussion of four types of aqueous solubility (three different thermodynamic solubilities with unknown solute structure, intrinsic solubility, solubility in physiological media at pH=7.4 and kinetic solubility), variety of molecular descriptors (from topological to quantum chemical), traditional statistical and machine learning methods as well as original QSPR models.

scholarly journals In silico prediction of hERG potassium channel blockage by chemical category approaches

2016 ◽  
Vol 5 (2) ◽  
pp. 570-582 ◽  
Author(s):  
Chen Zhang ◽  
Yuan Zhou ◽  
Shikai Gu ◽  
Zengrui Wu ◽  
Wenjie Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potassium Channel ◽  
In Silico ◽  
Molecular Descriptors ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Channel Blockage

A series of models of hERG blockage were built using five machine learning methods based on 13 molecular descriptors, five types of fingerprints and molecular descriptors combining fingerprints at four blockage thresholds.

Identification of DNA adduct formation of small molecules by molecular descriptors and machine learning methods

2012 ◽  
Vol 38 (4) ◽  
pp. 259-273
Author(s):  
Hanbing Rao ◽  
Xianyin Zeng ◽  
Yanying Wang ◽  
Hua He ◽  
Feng Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Molecular Descriptors ◽  
Adduct Formation ◽  
Dna Adduct ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

2020 ◽  
Author(s):  
Ely Setiawan ◽  
Mudasir Mudasir ◽  
Karna Wijaya
Keyword(s):  
Machine Learning ◽  
Critical Micelle Concentration ◽  
Cationic Surfactants ◽  
Molecular Descriptors ◽  
Consensus Model ◽  
Structure Property ◽  
Learning Methods ◽  
Data Set ◽  
Chemical Modeling ◽  
Machine Learning Methods

<p> A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R<sup>2</sup> = 0.95, q<sup>2 </sup>= 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R<sup>2</sup> = 0.87, q<sup>2</sup> = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.</p>

scholarly journals Machine learning methods to predict the crystallization propensity of small organic molecules

CrystEngComm ◽  
2020 ◽  
Vol 22 (16) ◽  
pp. 2817-2826
Author(s):  
Florbela Pereira
Keyword(s):  
Machine Learning ◽  
Organic Molecules ◽  
Molecular Descriptors ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Empirical Methods ◽  
Learning Methods ◽  
Small Organic Molecules ◽  
Chemical Structures ◽  
Machine Learning Methods

Machine learning algorithms were explored for the prediction of the crystallization propensity based on molecular descriptors and fingerprints generated from 2D chemical structures and 3D chemical structures optimized with empirical methods.

scholarly journals QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

2020 ◽  
Author(s):  
Ely Setiawan ◽  
Mudasir Mudasir ◽  
Karna Wijaya
Keyword(s):  
Machine Learning ◽  
Critical Micelle Concentration ◽  
Cationic Surfactants ◽  
Molecular Descriptors ◽  
Consensus Model ◽  
Structure Property ◽  
Learning Methods ◽  
Data Set ◽  
Chemical Modeling ◽  
Machine Learning Methods

<p> A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R<sup>2</sup> = 0.95, q<sup>2 </sup>= 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R<sup>2</sup> = 0.87, q<sup>2</sup> = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.</p>

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