QSAR Studies of HEPT Derivatives Using Support Vector Machines

Darnag, Rachid; Schmitzer, Andreea R.; Belmiloud, Yamina; Villemin, Didier; Jarid, Abdellah; Chait, Abderrahman; Seyagh, Maria; Cherqaoui, Driss

doi:10.1002/qsar.200810166

Cited by 11 publications

(6 citation statements)

References 37 publications

(21 reference statements)

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“…Another study examined SVM's effectiveness and prognostication power in HEPT derivative QSAR modeling. This investigation showed that SVM outperformed different approaches, including artificial neural networks, in terms of prediction [81]. SVM was also used to simulate phenethylamines' structure-activity relationships (SAR).…”

Section: Support Vector Machinementioning

confidence: 98%

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

Niazi

Mariam

2023

IJMS

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In modern drug discovery, the combination of chemoinformatics and quantitative structure–activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure–activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

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Section: Support Vector Machinementioning

confidence: 98%

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

Niazi

Mariam

2023

IJMS

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“…., a n ] T . By combining Equations (7) and (8), the eigenvalue problem can be represented by the following simple form:…”

Section: Kernel Principal Component Analysis (Kpca)mentioning

confidence: 99%

“…At present, many SAR modeling tools have been successfully employed to describe and build this relationship [2][3][4][5][6][7], for example, Artificial Neural Networks (ANN), Decision Tree (DT), Partial Least Squares (PLS), k-Nearest Neighbors (k-NN), Multiple Linear Regression (MLR), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). Among all these modeling methods, SVM has been one of the most popular modeling tools in the SAR study due to its prediction performance in terms of accuracy [7][8][9][10][11][12]. However, many researchers have pointed out that SVM also suffered from the problem of feature subset selection [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Combination of kernel PCA and linear support vector machine for modeling a nonlinear relationship between bioactivity and molecular descriptors

Cao

et al. 2011

Journal of Chemometrics

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In this paper, a two-step nonlinear classification algorithm is proposed to model the structure-activity relationship (SAR) between bioactivities and molecular descriptors of compounds, which consists of kernel principal component analysis (KPCA) and linear support vector machines (KPCA R LSVM). KPCA is used to remove some uninformative gradients such as noises and then exactly capture the latent structure of the training dataset using some new variables called the principal components in the kernel-defined feature space. LSVM makes full use of the maximal margin hyperplane to give the best generalization performance in the KPCA-transformed space. The combination of KPCA and LSVM can effectively improve the prediction performance compared with the linear SVM as well as two nonlinear methods. Three datasets related to different categorical bioactivities of compounds are used to evaluate the performance of KPCA R LSVM. The results show that our algorithm is competitive.

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“…Among them, inhibitors targeted on protease, integrase and reverse transcriptase (RT) are mainly investigated. [3][4][5][6][7][8][9][10] These compounds have shown great promise in the therapy of HIV infection, particularly in combination therapy because of their high selectivity and their diminished toxicity.…”

Section: Introductionmentioning

confidence: 99%

“…11 Currently there is considerable interest in the set up of QSAR of HIV-1 inhibitors on protease, integrase and RT respectively. [3][4][5] However, these experiments were mainly focused on specific types of targets individually. How to computationally (virtually) screen drug-like inhibitors jointly and simultaneously against multiple protein targets, taking into account protein-inhibitor dynamics with the QSAR modeling is still sparsely interpreted.…”

Section: Introductionmentioning

confidence: 99%

Multi‐target QSAR Study in the Analysis and Design of HIV‐1 Inhibitors

et al. 2010

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The multi-target quantitative structure-activity relationship (QSAR) study of human immunodeficiency virus (HIV-1) inhibitors was addressed by applying a simple, yet effective linear regression model based on the multi-task learning paradigm. QSAR studies were performed on three datasets of HIV-1 inhibitors targeted on protease, integrase and reverse transcriptase, respectively. By using the multi-task learning method, the synergy among different set of inhibitors was exploited and an efficient multi-target QSAR modeling for HIV-1 inhibitors was obtained. The general descriptor features and drug-like features for compound description were ranked according to their jointly importance in multi-target QSAR modeling, respectively. A SAReport for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is expected to provide useful clues for the design of novel multi-target HIV-1 inhibitors with increasing likelihood of successful therapies.

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QSAR Studies of HEPT Derivatives Using Support Vector Machines

Cited by 11 publications

References 37 publications

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

Combination of kernel PCA and linear support vector machine for modeling a nonlinear relationship between bioactivity and molecular descriptors

Multi‐target QSAR Study in the Analysis and Design of HIV‐1 Inhibitors

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