Applications of Machine Learning in Drug Target Discovery

2020 ◽  
Vol 21 (10) ◽  
pp. 790-803 ◽  
Author(s):  
Dongrui Gao ◽  
Qingyuan Chen ◽  
Yuanqi Zeng ◽  
Meng Jiang ◽  
Yongqing Zhang
Keyword(s):  
Machine Learning ◽  
Drug Development ◽  
Computational Methods ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Modern Drug ◽  
Applications Of Machine Learning

Drug target discovery is a critical step in drug development. It is the basis of modern drug development because it determines the target molecules related to specific diseases in advance. Predicting drug targets by computational methods saves a great deal of financial and material resources compared to in vitro experiments. Therefore, several computational methods for drug target discovery have been designed. Recently, machine learning (ML) methods in biomedicine have developed rapidly. In this paper, we present an overview of drug target discovery methods based on machine learning. Considering that some machine learning methods integrate network analysis to predict drug targets, network-based methods are also introduced in this article. Finally, the challenges and future outlook of drug target discovery are discussed.

scholarly journals An omics perspective on drug target discovery platforms

2019 ◽  
Vol 21 (6) ◽  
pp. 1937-1953 ◽  
Author(s):  
Jussi Paananen ◽  
Vittorio Fortino
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Knowledge Bases ◽  
Scientific Data ◽  
Data Sources ◽  
Biomedical Data ◽  
Drug Target Discovery ◽  
Data Repositories ◽  
Target Discovery ◽  
Omics Technologies

Abstract The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

scholarly journals A transcriptomics-guided drug target discovery strategy identifies novel receptor ligands for lung regeneration

2021 ◽  
Author(s):  
Xinhui Wu ◽  
Sophie Bos ◽  
Thomas M Conlon ◽  
Meshal Ansari ◽  
Vicky Verschut ◽  
...  
Keyword(s):  
Cigarette Smoke ◽  
Drug Target ◽  
Therapeutic Potential ◽  
Chronic Obstructive ◽  
Sequencing Analysis ◽  
Receptor Ligands ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Alveolar Epithelial

Currently, there is no pharmacological treatment targeting defective tissue repair in chronic disease. Here we utilized a transcriptomics-guided drug target discovery strategy using gene signatures of smoking-associated chronic obstructive pulmonary disease (COPD) and from mice chronically exposed to cigarette smoke, identifying druggable targets expressed in alveolar epithelial progenitors of which we screened the function in lung organoids. We found several drug targets with regenerative potential of which EP and IP prostanoid receptor ligands had the most significant therapeutic potential in restoring cigarette smoke-induced defects in alveolar epithelial progenitors in vitro and in vivo. Mechanistically, we discovered by using scRNA-sequencing analysis that circadian clock and cell cycle/apoptosis signaling pathways were enriched in alveolar epithelial progenitor cells in COPD patients and in a relevant model of COPD, which was prevented by PGE2 or PGI2 mimetics. Conclusively, specific targeting of EP and IP receptors offers therapeutic potential for injury to repair in COPD.

A Review of Recent Advances and Research on Drug Target Identification Methods

2019 ◽  
Vol 20 (3) ◽  
pp. 209-216 ◽  
Author(s):  
Yang Hu ◽  
Tianyi Zhao ◽  
Ningyi Zhang ◽  
Ying Zhang ◽  
Liang Cheng
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Machine Learning Algorithms ◽  
Topological Features ◽  
Drug Target Identification ◽  
Incomplete Datasets ◽  
Optimal Set

Background:From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue.Methods:We systematically review the recent work on identifying drug targets from the view of data and method. We compiled several databases that collect data more comprehensively and introduced several commonly used databases. Then divided the methods into two categories: biological experiments and machine learning, each of which is subdivided into different subclasses and described in detail.Results:Machine learning algorithms are the majority of new methods. Generally, an optimal set of features is chosen to predict successful new drug targets with similar properties. The most widely used features include sequence properties, network topological features, structural properties, and subcellular locations. Since various machine learning methods exist, improving their performance requires combining a better subset of features and choosing the appropriate model for the various datasets involved.Conclusion:The application of experimental and computational methods in protein drug target identification has become increasingly popular in recent years. Current biological and computational methods still have many limitations due to unbalanced and incomplete datasets or imperfect feature selection methods

scholarly journals Drug target discovery via network modeling: a mathematical model of the E. coli folate network response to trimethoprim

2019 ◽  
Author(s):  
Inderpreet Jalli ◽  
Sophia Lunt ◽  
Wenjia Xu ◽  
Carmen Lopez ◽  
Andreas Contreras ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Dihydrofolate Reductase ◽  
Drug Target ◽  
Drug Targets ◽  
Network Modeling ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
E Coli ◽  
Efficient Route ◽  
Insight Into

AbstractThe antibiotic trimethoprim targets the bacterial dihydrofolate reductase enzyme and subsequently affects the entire folate network. We present an expanded mathematical model of trimethoprim’s action on the Escherichia coli folate network that greatly improves upon Kwon et al. (2008). The improvement upon the Kwon Model lends greater insight into the effects of trimethoprim at higher resolution and accuracy. More importantly, the presented mathematical model enables drug target discovery in a way the earlier model could not. Using the improved mathematical model as a scaffold, we use parameter optimization to search for new drug targets that replicate the effect of trimethoprim. We present the model and model-scaffold strategy as an efficient route for drug target discovery.

scholarly journals TarDict: A RandomForestClassifier based software predicts drug-target interaction using SMILES

2021 ◽  
pp. bi202101
Author(s):  
Peter Habib ◽  
Alsamman Alsamman ◽  
Sameh Hassanein ◽  
Aladdin Hamwieh
Keyword(s):  
Machine Learning ◽  
Computational Model ◽  
Drug Target ◽  
Drug Targets ◽  
Cost Effective ◽  
New Drugs ◽  
Drug Target Discovery ◽  
Target Proteins ◽  
Biological Targets ◽  
Testing Dataset

The future of therapeutics depends on understanding the interaction between the chemical structure of the drug and the target protein that contributes to the etiology of the disease in order to improve drug discovery. Predicting the target of unknown drugs being investigated from already identified drug data is very important not only for understanding different processes of drug and molecular interactions but also for the development of new drugs. Using machine learning and published drug information we design an easy-to-use tool that predicts biological target proteins for medical drugs. TarDict is based on a chemical-simplified line-entry molecular input system called SMILES. It receives SMILES entries and returns a list of possible similar drugs as well as possible drug-targets. TarDict uses 20442 drug entries that have well-known biological targets to construct a prognostic computational model capable of predicting novel drug targets with an accuracy of 95%. We developed a machine learning approach to recommend target proteins to approved drug targets. We have shown that the proposed method is highly predictive on a testing dataset consisting of 4088 targets and 102 manually entered drugs. The proposed computational model is an efficient and cost-effective tool for drug target discovery and prioritization. Such novel tool could be used to enhance drug design, predict potential target and identify combination therapy crossroads.

scholarly journals TargetSeeker-MS: A Computational Method for Drug Target Discovery using Protein Separation Coupled to Mass Spectrometry

2019 ◽  
Author(s):  
Mathieu Lavallée-Adam ◽  
Alexander Pelletier ◽  
Jolene K. Diedrich ◽  
William Low ◽  
Antonio F. M. Pinto ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Drug Target ◽  
Drug Targets ◽  
Protein Separation ◽  
Computational Method ◽  
Drug Target Discovery ◽  
C Elegans ◽  
Computational Analyses ◽  
Novel Drug

ABSTRACTWhen coupled to mass spectrometry (MS), energetics-based protein separation (EBPS) techniques, such as thermal shift assay, have shown great potential to identify the targets of a drug on a proteome scale. Nevertheless, the computational analyses assessing the confidence of drug target predictions made by these methods have remained rudimentary and significantly differ depending on the protocol used to produce the data. To identify drug targets in datasets produced using different EBPS-MS techniques, we have developed a novel flexible computational approach named TargetSeeker-MS. We showed that TargetSeeker-MS reproducibly identifies known and novel drug targets in C. elegans and HEK293 samples that were treated with the fungicide benomyl and processed using two different EBPS techniques. We also validated a novel benomyl target in vitro. TargetSeeker-MS, which is available online, allows for the confident identification of targets of a drug on a proteome scale, thereby facilitating the evaluation of its clinical viability.

Abstract 2139: Enabling cancer drug target discovery through genome-scale identification of synthetic lethal paralog pairs

2021 ◽  
Author(s):  
Phoebe C. Parrish ◽  
James D. Thomas ◽  
Shriya Kamlapurkar ◽  
Robert K. Bradley ◽  
Alice H. Berger
Keyword(s):  
Drug Target ◽  
Cancer Drug ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Synthetic Lethal ◽  
Genome Scale

scholarly journals Application of genomics and proteomics in drug target discovery

2014 ◽  
Vol 13 (1) ◽  
pp. 198-204 ◽  
Author(s):  
H.M. Zhang ◽  
Z.R. Nan ◽  
G.Q. Hui ◽  
X.H. Liu ◽  
Y. Sun
Keyword(s):  
Drug Target ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Genomics And Proteomics

scholarly journals Compound2Drug – a Machine/deep Learning Tool for Predicting the Bioactivity of PubChem Compounds

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker ◽  
Rafal Madaj ◽  
Host Antony Davidd ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Molecular Docking ◽  
Drug Target ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Network Data ◽  
Ligand Interaction ◽  
Pubchem Compound ◽  
Protein Ligand Interaction

<p>Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction has been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the present work, compound-drug target interaction data set from bindingDB has been used to train machine learning/deep learning algorithms which are used to predict the drug targets for any PubChem compound queried by the user. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target. The tool also incorporates a feature to perform automated <i>In Silico</i> modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The programs fetches the structures of the compound and the predicted drug targets, prepares them for molecular docking using standard AutoDock Scripts that are part of MGLtools and performs molecular docking, protein-ligand interaction profiling of the targets and the compound and stores the visualized results in the working folder of the user. The program is hosted, supported and maintained at the following GitHub repository </p> <p><a href="https://github.com/bengeof/Compound2Drug">https://github.com/bengeof/Compound2Drug</a></p>

Sign in / Sign up

Export Citation Format

Share Document