DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction

Jin-jian, Jiang; Wang, Nian; Zhang, Youzhi; Zhang, Jun; Wang, Bing

doi:10.1155/2017/6340316

Cited by 17 publications

(11 citation statements)

References 38 publications

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“…AI/ML-based methods (we will frequently refer to them in this study as ML methods) are generally feature-based or similarity-based (see DTBA ML-based methods section). Feature-based AI/ML methods can be integrated with other approaches constructing “Ensemble system” as presented in Ezzat et al (2016), Jiang et al (2017), and Rayhan et al (2019). Thus, several comprehensive recent reviews summarized the different studies that predict DTIs using various techniques covering structure-based, similarity-based, network-based, and AI/ML-based methods as presented in Liu Y. et al (2016), Ezzat et al (2017, 2018, 2019), Rayhan et al (2017), Trosset and Cavé (2019), and Wan et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

et al. 2019

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The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. These computational approaches aim to narrow down the search space for novel DTIs and shed light on drug functioning context. Most methods developed to date use binary classification to predict if the interaction between a drug and its target exists or not. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If that strength is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities (DTBA) are of great value. In this study, we provide a comprehensive overview of the existing methods that predict DTBA. We focus on the methods developed using artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches, as well as related benchmark datasets and databases. Furthermore, guidance and recommendations are provided that cover the gaps and directions of the upcoming work in this research area. To the best of our knowledge, this is the first comprehensive comparison analysis of tools focused on DTBA with reference to AI/ML/DL.

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Section: Introductionmentioning

confidence: 99%

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

et al. 2019

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“…The method of obtaining the center of positive samples is discussed by experiment. Considering the implicit correlation of each dimension of drug-target pairs, the principal component analysis (PCA) is used firstly, and then the mean value of these orthogonal vectors is calculated as the positive center [ 50 ]. In this work, the results with PCA processing is marked as With-PCA, and the method of only calculating the mean of original features without PCA is marked as Without-PCA.…”

Section: Discussionmentioning

confidence: 99%

Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition

Wang

Lü

et al. 2020

IJMS

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The task of drug-target interaction (DTI) prediction plays important roles in drug development. The experimental methods in DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based methods are introduced, which are restricted by effective feature extraction and negative sampling. In this work, features with electrotopological state (E-state) fingerprints for drugs and amphiphilic pseudo amino acid composition (APAAC) for target proteins are tested. E-state fingerprints are extracted based on both molecular electronic and topological features with the same metric. APAAC is an extension of amino acid composition (AAC), which is calculated based on hydrophilic and hydrophobic characters to construct sequence order information. Using the combination of these feature pairs, the prediction model is established by support vector machines. In order to enhance the effectiveness of features, a distance-based negative sampling is proposed to obtain reliable negative samples. It is shown that the prediction results of area under curve for Receiver Operating Characteristic (AUC) are above 98.5% for all the three datasets in this work. The comparison of state-of-the-art methods demonstrates the effectiveness and efficiency of proposed method, which will be helpful for further drug development.

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“…Here, we used the first technique, where we created different splits of the same training dataset, and the random forest model is applied as a base classifier on all these split datasets. This approach is designed to improve our classification accuracy as well as to solve the issue of the class imbalance problem [19,20]. Here, the random forest model is taken as a base classifier because the performance of this model is better than other models.…”

Section: Proposed Ensemble-based Prediction Modelmentioning

confidence: 99%

Toxicity prediction of small drug molecules of aryl hydrocarbon receptor using a proposed ensemble model

Gupta¹,

Rana²

2019

Turk J Elec Eng & Comp Sci

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Quantitative structure-activity relationships and quantitative structure-property relationships have proved their usefulness for predicting toxicities of drug molecules regarding their biological activities. In silico toxicity prediction techniques are essential for reducing testing on rodents (in vivo) and for a less time-consuming and more cost-efficient alternative for the identification of toxic effects at an early stage of drug development. The authors aim to build a prediction model for better assessment of toxicity to quickly and efficiently test whether certain chemical compounds have the potential to disrupt the processes in the human body that may adversely affect human health. Here, we have proposed a computational method (in silico) for the toxicity prediction of small drug molecules using their various physicochemical properties (molecular descriptors) that can bind to the aryl hydrocarbon receptor. Pharmaceutical data exploration laboratory software is used for extracting the features of drug molecules. The dataset of the aryl hydrocarbon receptor contains 9008 drug molecules, where 1063 are active and 7945 are inactive, and each drug molecule contains 1444 features. It is a novel prediction model based on ensemble learning that can efficiently classify active (binding) and inactive (nonbinding) compounds of the dataset. In our proposed ensemble model, we primarily performed feature selection using the Boruta library in R, after which we resolved the class imbalance problem itself by ensemble learning where we divided the dataset into seven data frames, which have approximately equal numbers of active and inactive drug molecules. An ensemble model based upon the votes of seven random forest models is proposed, which gives an accuracy of 93.76%. K-fold cross-validation is conducted to measure the consistency of the model. Finally, the validity of the proposed ensemble model for some drug molecules of acquired immune deficiency syndrome therapy and androgen receptor has been proved.

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DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction

Cited by 17 publications

References 38 publications

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition

Toxicity prediction of small drug molecules of aryl hydrocarbon receptor using a proposed ensemble model

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