Machine Learning and Network Medicine approaches for Drug Repositioning for COVID-19

ABSTRACTDrug repositioning, i.e. identifying new uses for existing drugs and research compounds, is a cost-effective drug discovery strategy that is continuing to grow in popularity. Prioritizing and identifying drugs capable of being repositioned may improve the productivity and success rate of the drug discovery cycle, especially if the drug has already proven to be safe in humans. In previous work, we have shown that drugs that have been successfully repositioned have different chemical properties than those that have not. Hence, there is an opportunity to use machine learning to prioritize drug-like molecules as candidates for future repositioning studies. We have developed a feature engineering and machine learning that leverages data from publicly available drug discovery resources: RepurposeDB and DrugBank. ChemVec is the chemoinformatics-based feature engineering strategy designed to compile molecular features representing the chemical space of all drug molecules in the study. ChemVec was trained through a variety of supervised classification algorithms (Naïve Bayes, Random Forest, Support Vector Machines and an ensemble model combining the three algorithms). Models were created using various combinations of datasets as Connectivity Map based model, DrugBank Approved compounds based model, and DrugBank full set of compounds; of which RandomForest trained using Connectivity Map based data performed the best (AUC=0.674). Briefly, our study represents a novel approach to evaluate a small molecule for drug repositioning opportunity and may further improve discovery of pleiotropic drugs, or those to treat multiple indications.

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Prediction of drug-protein interaction and drug repositioning using machine learning model

10.1101/2020.07.29.218826 ◽

2020 ◽

Author(s):

Yu-Ting Lin ◽

Sheh-Yi Sheu ◽

Chen-Ching Lin

Keyword(s):

Machine Learning ◽

Betweenness Centrality ◽

Drug Repositioning ◽

Protein Network ◽

Training Data ◽

Support Vector ◽

Binding Interaction ◽

Binding Strength ◽

Edge Betweenness ◽

Protein Models

AbstractBackgroundTraditional drug development is time-consuming and expensive, while computer-aided drug repositioning can improve efficiency and productivity. In this study, we proposed a machine learning pipeline to predict the binding interaction between proteins and marketed or studied drugs. We then extended the predicted interactions to construct a protein network that could be applied to discover the potentially shared drugs between proteins and thus predict drug repositioning.MethodsBinding information between proteins and drugs from the Binding Database and the physicochemical properties of drugs from the ChEMBL database were used to build the machine learning models, i.e. support vector regression. We further measured proportionalities between proteins by the predicted binding affinity and introduced edge betweenness centrality to construct a protein similarity network for drug repositioning.ResultsAs the proof of concept, we demonstrated our machine learning approach is capable of reflecting the binding strength between drugs and the target protein. When comparing coefficients of protein models, we found proteins SYUA and TAU that may share common ligand which were not in our training data. Using the edge betweenness centrality network based on the prediction proportionality of protein models, we found a potential target, AK1C2, of aspirin and of which the binding interaction had been validated.ConclusionsOur study could not only be applied to drug repositioning by comparing protein models or searching the protein-protein network, but also to predict the binding strength once the sufficient experimental data was provided to train the protein models.

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Drug Repositioning for Schizophrenia and Depression/Anxiety Disorders: A Machine Learning Approach Leveraging Expression Data

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2018.2856535 ◽

2019 ◽

Vol 23 (3) ◽

pp. 1304-1315 ◽

Cited By ~ 16

Author(s):

Kai Zhao ◽

Hon-Cheong So

Keyword(s):

Machine Learning ◽

Anxiety Disorders ◽

Drug Repositioning ◽

Learning Approach ◽

Expression Data ◽

Machine Learning Approach

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Drug repositioning for psychiatric and neurological disorders through a network medicine approach

Translational Psychiatry ◽

10.1038/s41398-020-0827-5 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 2

Author(s):

Thomaz Lüscher Dias ◽

Viviane Schuch ◽

Patrícia Cristina Baleeiro Beltrão-Braga ◽

Daniel Martins-de-Souza ◽

Helena Paula Brentani ◽

...

Keyword(s):

Neurological Disorders ◽

Drug Repositioning ◽

Network Medicine

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Machine learning and network medicine

Journal of Cardiovascular Medicine ◽

10.2459/jcm.0000000000001103 ◽

2020 ◽

Vol Publish Ahead of Print ◽

Author(s):

Teresa Infante ◽

Marco Francone ◽

Maria L. De Rimini ◽

Carlo Cavaliere ◽

Raffaele Canonico ◽

...

Keyword(s):

Machine Learning ◽

Network Medicine

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Precision Psychiatry: Machine learning as a tool to find new pharmacological targets

Current Topics in Medicinal Chemistry ◽

10.2174/1568026621666211004095917 ◽

2021 ◽

Vol 21 ◽

Author(s):

João Rema ◽

Filipa Novais ◽

Diogo Telles-Correia

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Drug Repositioning ◽

Biological Data ◽

New Drugs ◽

Current Evidence ◽

Multiple Drug ◽

Pharmacological Targets ◽

New Findings ◽

Treatment Research

: There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs. Objective: The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets. Methods: Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review. Results : The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents. Conclusion: Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

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