QSAR Modeling of Nucleosides Against Amastigotes of <i>Leishmania donovani</i> Using Logistic Regression and Classification Tree

Oliveira, Kesley M. G.; Takahata, Yuji

doi:10.1002/qsar.200710172

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Applications of LR in QSAR studies includes modeling of nucleosides against amastigotes of Leishmania donovani [94], skin sensitization prediction [95][96][97], Tetrahymena pyriformis toxicity [14], classification of antibacterial activity [98], and sediment toxicity prediction [99].…”

Section: Gaussian Processesmentioning

confidence: 99%

Current Modeling Methods Used in QSAR/QSPR

Yee

Wei

2012

Statistical Modelling of Molecular Descriptors in QSAR/QSPR

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j1 diagnostic abilities. They can be used to predict the biological activity (e.g., IC 50 ) or class (e.g., inhibitor versus noninhibitors) of compounds before the actual biological testing. They can also be used in the analysis of structural characteristics that can give rise to the properties of interest.As illustrated in Figure 1.1, developing QSAR models starts with the collection of data for the property of interest while taking into consideration the quality of the data. It is necessary to exclude low-quality data as they will lower the quality of the model. Following that, representation of the collected molecules is done through the use of features, namely molecular descriptors, which describes important information of the molecules. There are many types of molecular descriptors but not all will be useful for a particular modeling task. Thus, uninformative or redundant molecular descriptors should be removed before the modeling process. Subsequently, for tuning and validation of the QSAR model, the full data set is divided into a training set and a testing set prior to learning.During the learning process, various modeling methods like multiple linear regression, logistic regression, and machine learning methods are used to build models that describe the empirical relationship between the structure and property of interest. The optimal model is obtained by searching for the optimal modeling parameters and feature subset simultaneously. This finalized model built from the optimal parameters will then undergo validation with a testing set to ensure that the model is appropriate and useful. Figure 1.1 General workflow of developing a QSAR model. 2j 1 Current Modeling Methods Used in QSAR/QSPR Figure 1.3 A simple structure showing the three layers of an artificial neural network. 6j 1 Current Modeling Methods Used in QSAR/QSPR Figure 1.4 GRNN is a neural network with four layers.

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Section: Gaussian Processesmentioning

confidence: 99%

Current Modeling Methods Used in QSAR/QSPR

Yee

Wei

2012

Statistical Modelling of Molecular Descriptors in QSAR/QSPR

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“…Here Mor26v is 3D-MoRSE descriptor corresponds to signal 26 , weighting with atomic Vander Waals volumes and it can distinguish the difference in molecular chirality[53].…”

mentioning

confidence: 99%

Development of QSAR Models on the Fouling-Release Performance of Silicone Oil-modified Siloxane Polyurethane Coatings

Khanam,

Casanola-Martin,

Daghighi

et al. 2024

Preprint

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Polymer coatings with a fouling release activity is an effective method to prevent marine biological fouling caused by large number of marine organisms (over 4000) and ensure stability and safety of marine facilities. In this study, a novel cheminformatics-based approach was employed to investigate the fouling-release performance of siloxane-PU coating system. The structure-fouling release activity relationship was modeled for a set of 18 siloxane PU coatings by incorporation phenylmethyl silicones oils (PMM-1025, PMM-1043, PMM-5021, PMM-6025, PMM-0021, PMM-0025 of 1, 2 and 5 wt%). A specific structural descriptors encoding approach was applied for these systems, based on a mixture-based encoding, to feed the complex polymeric systems in machine learning algorithms. Several predictive quantitative structure-fouling release relationships models were developed using machine learning techniques, from multiple linear regression to nonlinear random forest methods, followed by scoring them based on high performance accuracy and validation with rigorous internal fit R2 train values between 0.73 to 0.95 and external predictivity R2ext between 0.68 to 0.88. Random Forest method was the best nonlinear one for predicting diatom removal activity. This work indicates that both linear and nonlinear machine learning-based modeling can be beneficial to predict fouling release properties of polymer coatings with an effective fouling release performance.

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