Identification of Anticancer and Anti-inflammatory Drugs from Drug-target Interaction Descriptors by Machine Learning..

2022 ◽  
Vol 19 ◽  
Author(s):  
Songtao Huang ◽  
Yanrui Ding
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Hydrophobic Interactions ◽  
Drug Repositioning ◽  
Limited Information ◽  
Target Interaction ◽  
Interaction Sites ◽  
Drug Classification ◽  
Inflammatory Drugs ◽  
Anti Inflammatory

Background: Drug repositioning is an important subject in drug-disease research. In the past, most studies simply used drug descriptors as the feature vector to classify drugs or targets, or used qualitative data about drug-target or drug-disease to predict drug-target interactions. These data provide limited information for drug repositioning. Objective: Considering both drugs and targets and constructing quantitative drug-target interaction descriptors as a method of drug characteristics are of great significance to the study of drug repositioning. Methods: Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors were calculated to describe the drug-target interactions, and 22 important descriptors were screened for drug classification by SVM, LightGBM and MLP. Results: The accuracy of SVM, LightGBM and MLP reached 93.29%, 92.68% and 94.51%, their Matthews correlation coefficients reached 0.852, 0.840 and 0.882, and their areas under the ROC curve reached 0.977, 0.969 and 0.968, respectively. Conclusion: Using drug-target interaction descriptors to build machine learning models can obtain better results for drug classification. Number of atom pairs, force field, hydrophobic interactions and bSASA are the four types of key features for the classification of anticancer and anti-inflammatory drugs.

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction

2019 ◽  
Vol 20 (3) ◽  
pp. 194-202 ◽  
Author(s):  
Wen Zhang ◽  
Weiran Lin ◽  
Ding Zhang ◽  
Siman Wang ◽  
Jingwen Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Prediction Models ◽  
Prediction Methods ◽  
Crucial Issue ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Recent Advances ◽  
Drug Similarity

Background:The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.Results:In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.Conclusion:This study provides the guide to the development of computational methods for the drug-target interaction prediction.

scholarly journals DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Yihan Zhao ◽  
Kai Zheng ◽  
Baoyi Guan ◽  
Mengmeng Guo ◽  
Lei Song ◽  
...  
Keyword(s):  
Neural Networks ◽  
Molecular Mechanism ◽  
Drug Target ◽  
Drug Repositioning ◽  
Biological Data ◽  
Mapping Space ◽  
Target Interaction ◽  
Network Representation ◽  
Combination Methods ◽  
Low Dimensional

Abstract Background Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid. Methods A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fused the topology of complex networks and diverse information from heterogeneous data sources, and coped with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validated the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo. Results The experimental results showed that the DLDTI model achieved promising performance under fivefold cross-validations with AUC values of 0.9172, which was higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models. Conclusions DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.

scholarly journals DLDTI: A learning-based framework for drug-target interaction identification using neural networks and network representation

2020 ◽  
Author(s):  
Yihan Zhao ◽  
Kai Zheng ◽  
Baoyi Guan ◽  
Mengmeng Guo ◽  
Lei Song ◽  
...  
Keyword(s):  
Neural Networks ◽  
Molecular Mechanism ◽  
Drug Target ◽  
Drug Repositioning ◽  
Biological Data ◽  
Mapping Space ◽  
Target Interaction ◽  
Network Representation ◽  
Combination Methods ◽  
Low Dimensional

Abstract Background: Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid.Methods: A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI， was generated. The proposed approach simultaneously fuses the topology of complex networks and diverse information from heterogeneous data sources, and copes with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validate the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo.Results: The experimental results show that the DLDTI model achieves promising performance under 5-fold cross-validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models.Conclusions: DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.

scholarly journals A landscape for drug-target interactions based on network analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247018
Author(s):  
Edgardo Galan-Vasquez ◽  
Ernesto Perez-Rueda
Keyword(s):  
Drug Target ◽  
Target Genes ◽  
Metabolic Diseases ◽  
Drug Repositioning ◽  
Topological Analysis ◽  
Interaction Network ◽  
Clustering Coefficient ◽  
Connected Components ◽  
Target Interaction ◽  
Target Network

In this work, we performed an analysis of the networks of interactions between drugs and their targets to assess how connected the compounds are. For our purpose, the interactions were downloaded from the DrugBank database, and we considered all drugs approved by the FDA. Based on topological analysis of this interaction network, we obtained information on degree, clustering coefficient, connected components, and centrality of these interactions. We identified that this drug-target interaction network cannot be divided into two disjoint and independent sets, i.e., it is not bipartite. In addition, the connectivity or associations between every pair of nodes identified that the drug-target network is constituted of 165 connected components, where one giant component contains 4376 interactions that represent 89.99% of all the elements. In this regard, the histamine H1 receptor, which belongs to the family of rhodopsin-like G-protein-coupled receptors and is activated by the biogenic amine histamine, was found to be the most important node in the centrality of input-degrees. In the case of centrality of output-degrees, fostamatinib was found to be the most important node, as this drug interacts with 300 different targets, including arachidonate 5-lipoxygenase or ALOX5, expressed on cells primarily involved in regulation of immune responses. The top 10 hubs interacted with 33% of the target genes. Fostamatinib stands out because it is used for the treatment of chronic immune thrombocytopenia in adults. Finally, 187 highly connected sets of nodes, structured in communities, were also identified. Indeed, the largest communities have more than 400 elements and are related to metabolic diseases, psychiatric disorders and cancer. Our results demonstrate the possibilities to explore these compounds and their targets to improve drug repositioning and contend against emergent diseases.

scholarly journals DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246920
Author(s):  
Sk Mazharul Islam ◽  
Sk Md Mosaddek Hossain ◽  
Sumanta Ray
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Repositioning ◽  
Geometric Mean ◽  
Imbalanced Data ◽  
Drug Repurposing ◽  
Search Space ◽  
Classification Model ◽  
Target Interaction ◽  
Rough Approximation

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

scholarly journals Drug-Target Interaction Prediction Based on Adversarial Bayesian Personalized Ranking

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yihua Ye ◽  
Yuqi Wen ◽  
Zhongnan Zhang ◽  
Song He ◽  
Xiaochen Bo
Keyword(s):  
Partial Order ◽  
Matrix Factorization ◽  
Drug Target ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Learning To Rank ◽  
Prediction Performance ◽  
Target Interaction ◽  
Bayesian Personalized Ranking ◽  
Personalized Ranking

The prediction of drug-target interaction (DTI) is a key step in drug repositioning. In recent years, many studies have tried to use matrix factorization to predict DTI, but they only use known DTIs and ignore the features of drug and target expression profiles, resulting in limited prediction performance. In this study, we propose a new DTI prediction model named AdvB-DTI. Within this model, the features of drug and target expression profiles are associated with Adversarial Bayesian Personalized Ranking through matrix factorization. Firstly, according to the known drug-target relationships, a set of ternary partial order relationships is generated. Next, these partial order relationships are used to train the latent factor matrix of drugs and targets using the Adversarial Bayesian Personalized Ranking method, and the matrix factorization is improved by the features of drug and target expression profiles. Finally, the scores of drug-target pairs are achieved by the inner product of latent factors, and the DTI prediction is performed based on the score ranking. The proposed model effectively takes advantage of the idea of learning to rank to overcome the problem of data sparsity, and perturbation factors are introduced to make the model more robust. Experimental results show that our model could achieve a better DTI prediction performance.

scholarly journals A Machine Learning Approach to Identify Predictors of Potentially Inappropriate Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Use in Older Adults with Osteoarthritis

2020 ◽  
Vol 18 (1) ◽  
pp. 155
Author(s):  
Jayeshkumar Patel ◽  
Amit Ladani ◽  
Nethra Sambamoorthi ◽  
Traci LeMasters ◽  
Nilanjana Dwibedi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
The United States ◽  
Gradient Boosting ◽  
Study Cohort ◽  
Real World Data ◽  
Extreme Gradient Boosting ◽  
Inflammatory Drugs ◽  
Anti Inflammatory ◽  
Using Data

Evidence from some studies suggest that osteoarthritis (OA) patients are often prescribed non-steroidal anti-inflammatory drugs (NSAIDs) that are not in accordance with their cardiovascular (CV) or gastrointestinal (GI) risk profiles. However, no such study has been carried out in the United States. Therefore, we sought to examine the prevalence and predictors of potentially inappropriate NSAIDs use in older adults (age > 65) with OA using machine learning with real-world data from Optum De-identified Clinformatics® Data Mart. We identified a retrospective cohort of eligible individuals using data from 2015 (baseline) and 2016 (follow-up). Potentially inappropriate NSAIDs use was identified using the type (COX-2 selective vs. non-selective) and length of NSAIDs use and an individual’s CV and GI risk. Predictors of potentially inappropriate NSAIDs use were identified using eXtreme Gradient Boosting. Our study cohort comprised of 44,990 individuals (mean age 75.9 years). We found that 12.8% individuals had potentially inappropriate NSAIDs use, but the rate was disproportionately higher (44.5%) in individuals at low CV/high GI risk. Longer duration of NSAIDs use during baseline (AOR 1.02; 95% CI:1.02–1.02 for both non-selective and selective NSAIDs) was associated with a higher risk of potentially inappropriate NSAIDs use. Additionally, individuals with low CV/high GI (AOR 1.34; 95% CI:1.20–1.50) and high CV/low GI risk (AOR 1.61; 95% CI:1.34–1.93) were also more likely to have potentially inappropriate NSAIDs use. Heightened surveillance of older adults with OA requiring NSAIDs is warranted.

scholarly journals Compound2DeNovoDrugPropMax –a novel programmatic tool incorporating deep learning and in silico methods for automated bio-activity discovery for any compound of interest

2021 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Pavan Preetham Valluri ◽  
Harshmeet Singh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
In Silico ◽  
Drug Target ◽  
Learning Algorithms ◽  
Network Data ◽  
Ligand Interaction ◽  
Data Set ◽  
Target Interaction ◽  
Pubchem Compound

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 have been shown in the area of social networks through which highly customized suggestions are offered to social<br>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 presented work, compound-drug target interaction network data set from bindingDB has been used to train deep learning neural network and a multi class classification has been implemented to classify PubChem compound queried by the user into class labels of PBD IDs. This way target interaction prediction for PubChem compounds is carried out using deep learning. 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 interaction for the input CID. Further the tool also optimizes the compound of interest of the user toward drug likeness properties through a deep learning based structure optimization with a deep learning based<br>drug likeness optimization protocol. The tool also incorporates a feature to perform automated In Silico modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The program is hosted, supported and maintained at the following GitHub repository<div><br></div>https://github.com/bengeof/Compound2DeNovoDrugPropMax<br>

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