scholarly journals Dibenzoylhydrazines as Insect Growth Modulators: Topology-Based QSAR Modelling

2020 ◽  
Vol 12 (1) ◽  
pp. 28-40
Author(s):  
J.P. Doucet ◽  
A. Doucet-Panaye
Keyword(s):  
External Validation ◽  
Projection Pursuit ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Learning Approaches ◽  
Least Squares Regression ◽  
Insect Growth Regulators ◽  
Multilinear Regression ◽  
Insect Growth ◽  
Structural Variables

Dibenzoylhydrazines Xa-(C6H5)a-CO-N-(t-Bu)-NH-CO-(C6H5)b-Yb are efficient insect growth regulators with high activity and selectivity toward lepidopteran and coleopteran pests. For 123 congeneric molecules, a quantitative structure activity relationship model was built in the framework of the QSARINS package using 2D, Topology-based, PaDEL descriptors. Variable selection by GA-MLR allows building an efficient multilinear regression linking pEC50 values to nine structural variables. Robustness and quality of the model were carefully examined at various levels: data-fitting (recall), leave-one (or some) - out, internal and external validation (including random splitting), points not in depth investigated in previous works. Various Machine Learning approaches (Partial Least Squares Regression, Projection Pursuit Regression, Linear Support Vector Machine or Three Layer Perceptron Artificial Neural Network) confirm the validity of the analysis, giving highly consistent results of comparable quality, with only a slight advantage for the three-layer perceptron.

Graph Mining in Chemoinformatics

2011 ◽  
pp. 95-128 ◽  
Author(s):  
Hiroto Saigo ◽  
Koji Tsuda
Keyword(s):  
Graph Mining ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Quantitative Structure ◽  
Least Squares Regression ◽  
Lead Compounds ◽  
Least Angle Regression ◽  
Attributed Graphs ◽  
Vector Machines

In standard QSAR (Quantitative Structure Activity Relationship) approaches, chemical compounds are represented as a set of physicochemical property descriptors, which are then used as numerical features for classification or regression. However, standard descriptors such as structural keys and fingerprints are not comprehensive enough in many cases. Since chemical compounds are naturally represented as attributed graphs, graph mining techniques allow us to create subgraph patterns (i.e., structural motifs) that can be used as additional descriptors. In this chapter, the authors present theoretically motivated QSAR algorithms that can automatically identify informative subgraph patterns. A graph mining subroutine is embedded in the mother algorithm and it is called repeatedly to collect patterns progressively. The authors present three variations that build on support vector machines (SVM), partial least squares regression (PLS) and least angle regression (LARS). In comparison to graph kernels, our methods are more interpretable, thereby allows chemists to identify salient subgraph features to improve the druglikeliness of lead compounds.

scholarly journals Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya

2020 ◽  
Vol 187 ◽  
pp. 04001
Author(s):  
Ravipat Lapcharoensuk ◽  
Kitticheat Danupattanin ◽  
Chaowarin Kanjanapornprapa ◽  
Tawin Inkawee
Keyword(s):  
Machine Learning ◽  
Food Industry ◽  
Partial Least Squares Regression ◽  
Nir Spectroscopy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Least Squares Regression ◽  
Validation Set ◽  
Global Food

This research aimed to study the combination of NIR spectroscopy and machine learning for monitoring chilli sauce adulterated with papaya smoothie. The chilli sauce was produced by the famous community enterprise of chilli sauce processing in Thailand. The ingredients of the chilli sauce consisted of 45% chilli, 25% sugar, 20% garlic, 5% vinegar, and 5% salt. The chilli sauce sample was mixed with ripened papaya (Khaek Dam variety) smoothie with 9 levels from 10 to 90 %w/w. The NIR spectra of pure chilli sauce, papaya smoothie and 9 adulterated chilli sauce samples were recorded using FT-NIR spectrometer in the wavenumber range of 12500 and 4000 cm-1. Three machine learning algorithms were applied to develop a model for monitoring adulterated chilli sauce, including partial least squares regression (PLS), support vector machine (SVM), and backpropagation neural network (BPNN). All model presented performance of prediction in the validation set with R2al = 0.99 while RMSEP of PLS, SVM and BPNN were 1.71, 2.18 and 3.27% w/w respectively. This finding indicated that NIR spectroscopy coupled with machine learning approaches were shown to be an alternative technique to monitor papaya smoothie adulterated in chilli sauce in the global food industry.

scholarly journals QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 127
Author(s):  
Cosimo Toma ◽  
Alberto Manganaro ◽  
Giuseppa Raitano ◽  
Marco Marzo ◽  
Domenico Gadaleta ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Regression Models ◽  
External Validation ◽  
Inhalation Exposure ◽  
Quantitative Structure Activity Relationship ◽  
Learning Approaches ◽  
Chemical Safety ◽  
Qsar Models ◽  
Using Data

Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure–activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification models can now predict both human and rat carcinogenicity, but there are few models to quantitatively assess carcinogenicity in humans. To our knowledge, slope factor (SF), a parameter describing carcinogenicity potential used especially for human risk assessment of contaminated sites, has never been modeled for both inhalation and oral exposures. In this study, we developed classification and regression models for inhalation and oral SFs using data from the Risk Assessment Information System (RAIS) and different machine learning approaches. The models performed well in classification, with accuracies for the external set of 0.76 and 0.74 for oral and inhalation exposure, respectively, and r2 values of 0.57 and 0.65 in the regression models for oral and inhalation SFs in external validation. These models might therefore support regulators in (de)prioritizing substances for regulatory action and in weighing evidence in the context of chemical safety assessments. Moreover, these models are implemented on the VEGA platform and are now freely downloadable online.

scholarly journals STUDI HUBUNGAN KUANTITATIF STRUKTUR AKTIVITAS SENYAWA TURUNAN MEISOINDIGO SEBAGAI INHIBITOR CDK4

2016 ◽  
Vol 1 (2) ◽  
pp. 129
Author(s):  
Muhammad Arba ◽  
Riki Andriansyah ◽  
Messi Leonita
Keyword(s):  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Heat Of Formation ◽  
Electronic Energy ◽  
Cyclin Dependent Kinase ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Multilinear Regression Analysis ◽  
Qsar Models ◽  
Better Than

ABSTRAKTelah dilakukan analisis Hubungan Kuantitatif Struktur-Aktivitas (HKSA) senyawa turunan meisoindigo sebagai inhibitor Cyclin Dependent Kinase-4 (CDK4) menggunakan regresi multi linear untuk pemilihan variabel. Hasil penelitian menyatakan bahwa aktivitas penghambatan CDK4 dari senyawa turunan mesoindigo bergantung pada beberapa parameter, yaitu momen dipol, energi total, energi elektronik, panas pembentukan, dan kelarutan. Akurasi model HKSA yang diusulkan divalidasi baik dengan teknik validasi silang maupun dengan validasi eksternal. Hasil penelitian ini dapat digunakan untuk desain senyawa inhibitor CDK4 yang lebih baik dari turunan meisoindigo. Kata kunci: HKSA, meisoindigo, kanker, CDK4 ABSTRACTCyclin-dependent kinase 4 (CDK4) is an important target in the treatment of cancer. Exploring of compounds that can inhibit the activity of CDK4 is actively performed worldwide. This research was conducted to do Quantitative Structure-Activity Relationship (QSAR) analysis of meisoindigo derivative compounds as inhibitor for CDK4 in order to get QSAR equation, then it was further used to design new inhibitor based meisoindigo which has more potent and selective for CDK4. Data compound is divided into training set to build QSAR models and the test set to validate the model. Calculation was done by MOE2009.10 descriptor and multilinear regression analysis, SPSS19.0. The results showed that the inhibitory activity of mesoindigo derived compounds toward CDK4 was depended on several dipole moment, total energy, electronic energy, heat of formation, and solubility. The accuracy of QSAR models proposed validated by cross validation techniques and with external validation. The results of this study can be used to design a new CDK4 inhibitor compound better than meisoindigo derivative Keywords: QSAR, meisoindigo, cancer, CDK4

QSAR Studies of Halogenated Pyrimidine Derivatives as Inhibitors of Human Dihydroorotate Dehydrogenase Using Modified Bee Algorithm

2018 ◽  
Vol 21 (5) ◽  
pp. 381-387 ◽  
Author(s):  
Hossein Atabati ◽  
Kobra Zarei ◽  
Hamid Reza Zare-Mehrjardi
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Dihydroorotate Dehydrogenase ◽  
Pyrimidine Derivatives ◽  
Qsar Studies ◽  
Bee Algorithm ◽  
Test Sets ◽  
Leave One Out

Aim and Objective: Human dihydroorotate dehydrogenase (DHODH) catalyzes the fourth stage of the biosynthesis of pyrimidines in cells. Hence it is important to identify suitable inhibitors of DHODH to prevent virus replication. In this study, a quantitative structure-activity relationship was performed to predict the activity of one group of newly synthesized halogenated pyrimidine derivatives as inhibitors of DHODH. Materials and Methods: Molecular structures of halogenated pyrimidine derivatives were drawn in the HyperChem and then molecular descriptors were calculated by DRAGON software. Finally, the most effective descriptors for 32 halogenated pyrimidine derivatives were selected using bee algorithm. Results: The selected descriptors using bee algorithm were applied for modeling. The mean relative error and correlation coefficient were obtained as 2.86% and 0.9627, respectively, while these amounts for the leave one out−cross validation method were calculated as 4.18% and 0.9297, respectively. The external validation was also conducted using two training and test sets. The correlation coefficients for the training and test sets were obtained as 0.9596 and 0.9185, respectively. Conclusion: The results of modeling of present work showed that bee algorithm has good performance for variable selection in QSAR studies and its results were better than the constructed model with the selected descriptors using the genetic algorithm method.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Rational Design of Colchicine Derivatives as anti-HIV Agents via QSAR and Molecular Docking

2019 ◽  
Vol 15 (4) ◽  
pp. 328-340 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Napat Songtawee ◽  
Suphakit Siriwong ◽  
Supaluk Prachayasittikul ◽  
Chanin Nantasenamat ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Rational Design ◽  
External Validation ◽  
Rational Drug Design ◽  
Support Vector ◽  
Data Set ◽  
Qsar Models ◽  
Anti Hiv Agents ◽  
Anti Hiv ◽  
Colchicine Derivatives

Background: Human immunodeficiency virus (HIV) is an infective agent that causes an acquired immunodeficiency syndrome (AIDS). Therefore, the rational design of inhibitors for preventing the progression of the disease is required. Objective: This study aims to construct quantitative structure-activity relationship (QSAR) models, molecular docking and newly rational design of colchicine and derivatives with anti-HIV activity. Methods: A data set of 24 colchicine and derivatives with anti-HIV activity were employed to develop the QSAR models using machine learning methods (e.g. multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM)), and to study a molecular docking. Results: The significant descriptors relating to the anti-HIV activity included JGI2, Mor24u, Gm and R8p+ descriptors. The predictive performance of the models gave acceptable statistical qualities as observed by correlation coefficient (Q2) and root mean square error (RMSE) of leave-one out cross-validation (LOO-CV) and external sets. Particularly, the ANN method outperformed MLR and SVM methods that displayed LOO−CV 2 Q and RMSELOO-CV of 0.7548 and 0.5735 for LOOCV set, and Ext 2 Q of 0.8553 and RMSEExt of 0.6999 for external validation. In addition, the molecular docking of virus-entry molecule (gp120 envelope glycoprotein) revealed the key interacting residues of the protein (cellular receptor, CD4) and the site-moiety preferences of colchicine derivatives as HIV entry inhibitors for binding to HIV structure. Furthermore, newly rational design of colchicine derivatives using informative QSAR and molecular docking was proposed. Conclusion: These findings serve as a guideline for the rational drug design as well as potential development of novel anti-HIV agents.

scholarly journals Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

2021 ◽  
Vol 10 (4) ◽  
pp. 199
Author(s):  
Francisco M. Bellas Aláez ◽  
Jesus M. Torres Palenzuela ◽  
Evangelos Spyrakos ◽  
Luis González Vilas
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Prediction Models ◽  
Support Vector ◽  
False Alarms ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Rías Baixas ◽  
New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

Sign in / Sign up

Export Citation Format

Share Document