Novel drug target identification for the treatment of dementia using multi-relational association mining

Abstract To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug–target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug–target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, ‘Asthma’ ‘Hypertension’, and ‘Dementia’. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.

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Drug Target Identification with Machine Learning: How to Choose Negative Examples

International Journal of Molecular Sciences ◽

10.3390/ijms22105118 ◽

2021 ◽

Vol 22 (10) ◽

pp. 5118

Author(s):

Matthieu Najm ◽

Chloé-Agathe Azencott ◽

Benoit Playe ◽

Véronique Stoven

Keyword(s):

Machine Learning ◽

Drug Target ◽

Target Identification ◽

Target Prediction ◽

False Positives ◽

Machine Learning Algorithms ◽

Statistical Bias ◽

Protein Targets ◽

Drug Target Identification ◽

Approved Drugs

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

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Computational drug target identification in Streptococcus pneumoniae using available microarray data

Journal of Pharmacy Research ◽

10.1016/j.jopr.2013.04.003 ◽

2013 ◽

Vol 6 (4) ◽

pp. 404-408

Author(s):

Limpon Bora

Keyword(s):

Streptococcus Pneumoniae ◽

Microarray Data ◽

Drug Target ◽

Target Identification ◽

Drug Target Identification

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Some Perspectives on Network Modeling in Therapeutic Target Prediction

Biomedical Engineering and Computational Biology ◽

10.4137/becb.s10793 ◽

2013 ◽

Vol 5 ◽

pp. BECB.S10793 ◽

Cited By ~ 3

Author(s):

Reka Albert ◽

Bhaskar DasGupta ◽

Nasim Mobasheri

Keyword(s):

Therapeutic Target ◽

Drug Target ◽

Biological Network ◽

Target Identification ◽

Target Prediction ◽

Identification Problem ◽

Pharmaceutical Companies ◽

Specific Behavior ◽

Drug Target Identification ◽

Key Steps

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.

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Drug Target Identification in Tissues by Thermal Proteome Profiling

The Annual Review of Pharmacology and Toxicology ◽

10.1146/annurev-pharmtox-052120-013205 ◽

2021 ◽

Vol 62 (1) ◽

Author(s):

André Mateus ◽

Nils Kurzawa ◽

Jessica Perrin ◽

Giovanna Bergamini ◽

Mikhail M. Savitski

Keyword(s):

Drug Target ◽

Target Identification ◽

Annual Review ◽

Publication Date ◽

Proteome Profiling ◽

Biological Phenomena ◽

Target Deconvolution ◽

Drug Target Identification ◽

Recent Developments ◽

Fundamental Biology

Drug target deconvolution can accelerate the drug discovery process by identifying a drug's targets (facilitating medicinal chemistry efforts) and off-targets (anticipating toxicity effects or adverse drug reactions). Multiple mass spectrometry–based approaches have been developed for this purpose, but thermal proteome profiling (TPP) remains to date the only one that does not require compound modification and can be used to identify intracellular targets in living cells. TPP is based on the principle that the thermal stability of a protein can be affected by its interactions. Recent developments of this approach have expanded its applications beyond drugs and cell cultures to studying protein-drug interactions and biological phenomena in tissues. These developments open up the possibility of studying drug treatment or mechanisms of disease in a holistic fashion, which can result in the design of better drugs and lead to a better understanding of fundamental biology. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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