A non-conformational QSAR study for plant-derived larvicides against Zika Aedes aegypti L. vector

Saavedra, Laura; Romanelli, Gustavo Pablo; Duchowicz, Pablo R.

doi:10.1007/s11356-019-06630-9

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…Additionally, multicollinearity between descriptors was assessed using the variance inflation factor ( VIF ). The VIF of each descriptor should be less than 10 to avoid excessive inter-correlation between descriptors [ 69 ]. The MLR-1 and MLR-2 models were developed by establishing the relationship between measured log K ca values and the best descriptor combinations based on dataset (I) and dataset (II), respectively.…”

Section: Methodsmentioning

confidence: 99%

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Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective

Tao,

Zhu

2024

Molecules

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Accurately predicting plant cuticle–air partition coefficients (Kca) is essential for assessing the ecological risk of organic pollutants and elucidating their partitioning mechanisms. The current work collected 255 measured Kca values from 25 plant species and 106 compounds (dataset (I)) and averaged them to establish a dataset (dataset (II)) containing Kca values for 106 compounds. Machine-learning algorithms (multiple linear regression (MLR), multi-layer perceptron (MLP), k-nearest neighbors (KNN), and gradient-boosting decision tree (GBDT)) were applied to develop eight QSPR models for predicting Kca. The results showed that the developed models had a high goodness of fit, as well as good robustness and predictive performance. The GBDT-2 model (Radj2 = 0.925, QLOO2 = 0.756, QBOOT2 = 0.864, Rext2 = 0.837, Qext2 = 0.811, and CCC = 0.891) is recommended as the best model for predicting Kca due to its superior performance. Moreover, interpreting the GBDT-1 and GBDT-2 models based on the Shapley additive explanations (SHAP) method elucidated how molecular properties, such as molecular size, polarizability, and molecular complexity, affected the capacity of plant cuticles to adsorb organic pollutants in the air. The satisfactory performance of the developed models suggests that they have the potential for extensive applications in guiding the environmental fate of organic pollutants and promoting the progress of eco-friendly and sustainable chemical engineering.

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Section: Methodsmentioning

confidence: 99%

Section: Model Development and Validationmentioning

confidence: 99%

Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective

Tao,

Zhu

2024

Molecules

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“…However, it is rare to specify the optimum explanatory variable in advance. Further, in the case of the best subset selection or round-robin method, in which analysis is performed by utilizing all the combinations of the explanatory variables, there are 2 k − 1 combinations of k explanatory variables, resulting in a huge calculation cost (Noon et al, 2011;Saavedra et al, 2020). Additionally, based on the usefulness of each univariate regression coefficient, there are some sequential selection methods, which include forward−backward stepwise selection method, forward stepwise selection method, backward stepwise selection method, and backward−forward stepwise selection method, that sequentially increase or decrease the explanatory variables individually (Goodarzi et al, 2012;Fatima et al, 2018;Fatima et al, 2019;Hrynkiewicz et al, 2019;Fatima and Agarwal, 2020;McCann et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning

Matsuzaka¹,

Uesawa²

2022

Current Issues in Molecular Biology

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The quantitative structure-activity relationship (QSAR) approach has been used in numerous chemical compounds as in silico computational assessment for a long time. Further, owing to the high-performance modeling of QSAR, machine learning methods have been developed and upgraded. Particularly, the threedimensional structure of chemical compounds has been gaining increasing attention owing to the representation of a large amount of information. However, only many of feature extraction is impossible to build models with the high-ability of the prediction. Thus, suitable extraction and effective selection of features are essential for models with excellent performance. Recently, the deep learning method has been employed to construct prediction models with very high performance using big data, especially, in the field of classification. Therefore, in this study, we developed a molecular image-based novel QSAR approach, called DeepSnap-Deep learning approach for designing high-performance models. In addition, this DeepSnap-Deep learning approach outperformed the conventional machine learnings when they are compared. Herein, we discuss the advantage and disadvantages of the machine learnings as well as the availability of the DeepSnap-Deep learning approach.

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Overproduce and select, or determine optimal molecular descriptor subset via configuration space optimization? Application to the prediction of ecotoxicological endpoints

García-González

Marrero-Ponce

Brizuela

et al. 2023

Molecular Informatics

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Predicting the likely biological activity (or property) of compounds is a fundamental and challenging task in the drug discovery process. Current computational methodologies aim to improve their predictive accuracies by using deep learning (DL) approaches. However, non‐DL based approaches for small‐ and medium‐sized chemical datasets have demonstrated to be most suitable for. In this approach, an initial universe of molecular descriptors (MDs) is first calculated, then different feature selection algorithms are applied, and finally, one or several predictive models are built. Herein we demonstrate that this traditional approach may miss relevant information by assuming that the initial universe of MDs codifies all relevant aspects for the respective learning task. We argue that this limitation is mainly because of the constrained intervals of the parameters used in the algorithms that compute MDs, parameters that define the Descriptor Configuration Space (DCS). We propose to relax these constraints in an open CDS approach, so that a larger universe of MDs can be initially considered. We model the generation of MDs as a multicriteria optimization problem and tackle it with a variant of the standard genetic algorithm. As a novel component, the fitness function is computed by aggregating four criteria via the Choquet integral. Experimental results show that the proposed approach generates a meaningful DCS by improving state‐of‐the‐art approaches in most of the benchmarking chemical datasets accounted for.

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A non-conformational QSAR study for plant-derived larvicides against Zika Aedes aegypti L. vector

Cited by 7 publications

References 42 publications

Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective

Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective

A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning

Overproduce and select, or determine optimal molecular descriptor subset via configuration space optimization? Application to the prediction of ecotoxicological endpoints

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