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Journal articles on the topic 'Drug Side Effect Prediction'

Author: Grafiati

Published: 10 March 2023

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1

Hu, Baofang, Hong Wang, and Zhenmei Yu. "Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network." Molecules 24, no. 20 (2019): 3668. http://dx.doi.org/10.3390/molecules24203668.

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Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug–target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential

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Seo, Sukyung, Taekeon Lee, Mi-hyun Kim, and Youngmi Yoon. "Prediction of Side Effects Using Comprehensive Similarity Measures." BioMed Research International 2020 (February 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/1357630.

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Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be util

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Kim, Jinwoo, and Miyoung Shin. "A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction." Applied Sciences 13, no. 5 (2023): 2842. http://dx.doi.org/10.3390/app13052842.

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Predicting the side effects caused by drug combinations may facilitate the prescription of multiple medications in a clinical setting. So far, several prediction models of multidrug side effects based on knowledge graphs have been developed, showing good performance under constrained test conditions. However, these models usually focus on relationships between neighboring nodes of constituent drugs rather than whole nodes, and do not fully exploit the information about the occurrence of single drug side effects. The lack of learning the information on such relationships and single drug data ma

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Arshed, Muhammad Asad, Shahzad Mumtaz, Omer Riaz, Waqas Sharif, and Saima Abdullah. "A Deep Learning Framework for Multi Drug Side Effects Prediction with Drug Chemical Substructure." Vol 4 Issue 1 4, no. 1 (2022): 19–31. http://dx.doi.org/10.33411/ijist/2022040102.

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Nowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of

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Mohd Ali, Yousoff Effendy, Kiam Heong Kwa, and Kurunathan Ratnavelu. "Predicting new drug indications from network analysis." International Journal of Modern Physics C 28, no. 09 (2017): 1750118. http://dx.doi.org/10.1142/s0129183117501182.

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This work adapts centrality measures commonly used in social network analysis to identify drugs with better positions in drug-side effect network and drug-indication network for the purpose of drug repositioning. Our basic hypothesis is that drugs having similar phenotypic profiles such as side effects may also share similar therapeutic properties based on related mechanism of action and vice versa. The networks were constructed from Side Effect Resource (SIDER) 4.1 which contains 1430 unique drugs with side effects and 1437 unique drugs with indications. Within the giant components of these n

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Zhao, Xian, Lei Chen, Zi-Han Guo, and Tao Liu. "Predicting Drug Side Effects with Compact Integration of Heterogeneous Networks." Current Bioinformatics 14, no. 8 (2019): 709–20. http://dx.doi.org/10.2174/1574893614666190220114644.

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Background: The side effects of drugs are not only harmful to humans but also the major reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical companies. However, detecting the side effects for a given drug via traditional experiments is time- consuming and expensive. In recent years, several computational methods have been proposed to predict the side effects of drugs. However, most of the methods cannot effectively integrate the heterogeneous properties of drugs. Methods: In this study, we adopted a network embedding method, Mashup, to extract essential and informa

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Duffy, Áine, Marie Verbanck, Amanda Dobbyn, et al. "Tissue-specific genetic features inform prediction of drug side effects in clinical trials." Science Advances 6, no. 37 (2020): eabb6242. http://dx.doi.org/10.1126/sciadv.abb6242.

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Adverse side effects often account for the failure of drug clinical trials. We evaluated whether a phenome-wide association study (PheWAS) of 1167 phenotypes in >360,000 U.K. Biobank individuals, in combination with gene expression and expression quantitative trait loci (eQTL) in 48 tissues, can inform prediction of drug side effects in clinical trials. We determined that drug target genes with five genetic features—tissue specificity of gene expression, Mendelian associations, phenotype- and tissue-level effects of genome-wide association (GWA) loci driven by eQTL, and genetic constraint—c

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Chen, Lei, Tao Huang, Jian Zhang, et al. "Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/485034.

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A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our m

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Zhou, Mengshi, Yang Chen, and Rong Xu. "A Drug-Side Effect Context-Sensitive Network approach for drug target prediction." Bioinformatics 35, no. 12 (2018): 2100–2107. http://dx.doi.org/10.1093/bioinformatics/bty906.

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Shaked, Itay, Matthew A. Oberhardt, Nir Atias, Roded Sharan, and Eytan Ruppin. "Metabolic Network Prediction of Drug Side Effects." Cell Systems 2, no. 3 (2016): 209–13. http://dx.doi.org/10.1016/j.cels.2016.03.001.

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Liang, Haiyan, Lei Chen, Xian Zhao, and Xiaolin Zhang. "Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy." Computational and Mathematical Methods in Medicine 2020 (May 9, 2020): 1–16. http://dx.doi.org/10.1155/2020/1573543.

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Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computational methods is an alternative way. Some studies paired the drug and side effect as a sample, thereby modeling the problem as a binary classification problem. However, the selection of negative samples is a key problem in this case. In this study, a novel negative sample selection strategy was designed

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Seo, Sukyung, Taekeon Lee, and Youngmi Yoon. "Prediction of Drug Side Effects Based on Drug-Related Information." Journal of Korean Institute of Information Technology 17, no. 12 (2019): 21–28. http://dx.doi.org/10.14801/jkiit.2019.17.12.21.

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Chen, Y. H., Y. T. Shih, C. S. Chien, and C. S. Tsai. "Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach." PLOS ONE 17, no. 12 (2022): e0266435. http://dx.doi.org/10.1371/journal.pone.0266435.

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We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer perceptron (MLP) denoted by GCNMLP to explore the potential side effects of drugs. Here the SIDER, OFFSIDERS, and FAERS are used as the datasets. We integrate the drug information with similar characteristics from the datasets of known drugs and side effect networks. The heterogeneous graph networks explore the potential side effects of drugs by inferring the relationship between similar drugs and related side effects. This novel in silico method will shorten the time spent in uncovering the unseen side effect

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Zheng, Yi, Wentao Zhao, Chengcheng Sun, and Qian Li. "Drug Side-Effect Prediction Using Heterogeneous Features and Bipartite Local Models." Computers, Materials & Continua 60, no. 2 (2019): 481–96. http://dx.doi.org/10.32604/cmc.2019.05536.

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Niu, Yanqing, and Wen Zhang. "Quantitative prediction of drug side effects based on drug-related features." Interdisciplinary Sciences: Computational Life Sciences 9, no. 3 (2017): 434–44. http://dx.doi.org/10.1007/s12539-017-0236-5.

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Lounkine, Eugen, Michael J. Keiser, Steven Whitebread, et al. "Large-scale prediction and testing of drug activity on side-effect targets." Nature 486, no. 7403 (2012): 361–67. http://dx.doi.org/10.1038/nature11159.

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Pancino, Niccolò, Yohann Perron, Pietro Bongini, and Franco Scarselli. "Drug Side Effect Prediction with Deep Learning Molecular Embedding in a Graph-of-Graphs Domain." Mathematics 10, no. 23 (2022): 4550. http://dx.doi.org/10.3390/math10234550.

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Drug side effects (DSEs), or adverse drug reactions (ADRs), constitute an important health risk, given the approximately 197,000 annual DSE deaths in Europe alone. Therefore, during the drug development process, DSE detection is of utmost importance, and the occurrence of ADRs prevents many candidate molecules from going through clinical trials. Thus, early prediction of DSEs has the potential to massively reduce drug development times and costs. In this work, data are represented in a non-euclidean manner, in the form of a graph-of-graphs domain. In such a domain, structures of molecule are r

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Guney, Emre. "Revisiting Cross-Validation of Drug Similarity Based Classifiers Using Paired Data." Genomics and Computational Biology 4, no. 1 (2017): 100047. http://dx.doi.org/10.18547/gcb.2018.vol4.iss1.e100047.

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Following the recent availability of high-throughput data for drug discovery, computational methods, especially machine learning based approaches, have gained remarkable attention. A number of studies use chemical, target and side effect similarity between drugs to build knowledge-based models that predict drug indications and drug-drug interactions. In light of previous works demonstrating the perils of cross-validation using paired data, in this study, we employ a disjoint cross validation approach for similarity-based drug-drug interaction (DDI) prediction and we investigate the prediction

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Huang, Wei, Chunyan Li, Ying Ju, and Yan Gao. "The Next Generation of Machine Learning in DDIs Prediction." Current Pharmaceutical Design 27, no. 23 (2021): 2728–36. http://dx.doi.org/10.2174/1381612827666210127122312.

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Drug-drug interactions may occur when combining two or more drugs may cause some adverse events such as cardiotoxicity, central neurotoxicity, hepatotoxicity, etc. However, a large number of researchers who are proficient in pharmacokinetics and pharmacodynamics have been engaged in drug assays and trying to find out the side effects of all kinds of drug combinations. However, at the same time, the number of new drugs is increasing dramatically, and the drug assay is an expensive and time-consuming process. It is impossible to find all the adverse reactions through drug experiments. Therefore,

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Lim, Seungsoo, Hayon Lee, and Youngmi Yoon. "Prediction of New Drug-Side Effect Relation using Word2Vec Model-based Word Similarity." Journal of Korean Institute of Information Technology 18, no. 11 (2020): 25–33. http://dx.doi.org/10.14801/jkiit.2020.18.11.25.

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Yamanishi, Yoshihiro, Edouard Pauwels, and Masaaki Kotera. "Drug Side-Effect Prediction Based on the Integration of Chemical and Biological Spaces." Journal of Chemical Information and Modeling 52, no. 12 (2012): 3284–92. http://dx.doi.org/10.1021/ci2005548.

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Zhou, Mengshi, Chunlei Zheng, and Rong Xu. "Combining phenome-driven drug-target interaction prediction with patients’ electronic health records-based clinical corroboration toward drug discovery." Bioinformatics 36, Supplement_1 (2020): i436—i444. http://dx.doi.org/10.1093/bioinformatics/btaa451.

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Abstract Motivation Predicting drug–target interactions (DTIs) using human phenotypic data have the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinica

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Che, Jingang, Lei Chen, Zi-Han Guo, Shuaiqun Wang, and Aorigele. "Drug Target Group Prediction with Multiple Drug Networks." Combinatorial Chemistry & High Throughput Screening 23, no. 4 (2020): 274–84. http://dx.doi.org/10.2174/1386207322666190702103927.

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Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target grou

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Mohanapriya, D., and Dr R. Beena. "Predicting Drug Indications and Side Effects Using Deep Learning and Transfer Learning." Alinteri Journal of Agriculture Sciences 36, no. 1 (2021): 281–89. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21042.

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In the area of biology, text mining is commonly used since it obtains the unknown relationship among medicines, phenotypes and syndromes from much information. Enhanced Topic modeling with Improved Predict drug Indications and Side effects using Topic modelling and Natural language processing (ETP-IPISTON) has been employed to predict the drug-phenotype and drug-side effect association. Initially, corpus documents are collected from the literature data and the topics in the data are modeled using logistic Linear Discriminative Analysis (LDA) and Bi-directional Long-Short Term Memory-Conditiona

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Mower, Justin, Devika Subramanian, and Trevor Cohen. "Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications." Journal of the American Medical Informatics Association 25, no. 10 (2018): 1339–50. http://dx.doi.org/10.1093/jamia/ocy077.

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Abstract Objective The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring. Methods Using ≈80 million concept-relationship-concept triples extracted from the literature using the SemRep Natural Language Processing system, distributed vector representations (embeddings) were generated for concepts as functions of their relationships utilizing two unsupervised representational approaches. Embeddings for drugs and sid

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Yao, Yuanzhe, Zeheng Wang, Liang Li, et al. "An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example." Computational and Mathematical Methods in Medicine 2019 (October 1, 2019): 1–7. http://dx.doi.org/10.1155/2019/8617503.

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In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book, and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription

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Dykeman, J., M. Lowerison, P. Faris, et al. "Prediction of Antiepileptic Drug Side Effects in Patients with Epilepsy (S06.007)." Neurology 78, Meeting Abstracts 1 (2012): S06.007. http://dx.doi.org/10.1212/wnl.78.1_meetingabstracts.s06.007.

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Kanji, Rakesh, Abhinav Sharma, and Ganesh Bagler. "Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs." Molecular BioSystems 11, no. 11 (2015): 2900–2906. http://dx.doi.org/10.1039/c5mb00312a.

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Knowing the importance of identification of drug features that are critical for specifying their adverse effects, we propose a generalized ordinary canonical correlation analysis model that integrates the target profiles and chemical profiles of drugs.

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Wilson, Jennifer L., Alessio Gravina, and Kevin Grimes. "From random to predictive: a context-specific interaction framework improves selection of drug protein–protein interactions for unknown drug pathways." Integrative Biology 14, no. 1 (2022): 13–24. http://dx.doi.org/10.1093/intbio/zyac002.

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Abstract With high drug attrition, protein–protein interaction (PPI) network models are attractive as efficient methods for predicting drug outcomes by analyzing proteins downstream of drug targets. Unfortunately, these methods tend to overpredict associations and they have low precision and prediction performance; performance is often no better than random (AUROC ~0.5). Typically, PPI models identify ranked phenotypes associated with downstream proteins, yet methods differ in prioritization of downstream proteins. Most methods apply global approaches for assessing all phenotypes. We hypothesi

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Wang, Chen, and Lukasz Kurgan. "Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome." Briefings in Bioinformatics 20, no. 6 (2018): 2066–87. http://dx.doi.org/10.1093/bib/bby069.

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AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and

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Paiman, Arif, Ahmad Mohammad, and Mubashar Rehman. "Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction." Global Drug Design & Development Review II, no. I (2017): 1–8. http://dx.doi.org/10.31703/gdddr.2017(ii-i).01.

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In modern day, Data on different diseases and drug substances with their properties like modification, side effects, and dose requires documentation data and building library exploring, such library with vast information in every aspect needs computational methods used in CADD. Recognition of specific targets for the drug tested and defining pharmacological activity of a drug candidate based on the structure of both drug and its target, finding outside effects of drugs at the molecular level and calculation of toxicity caused by metabolism of drug applications of Computer aided drug design in

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Hwang, Youhyeon, Min Oh, and Youngmi Yoon. "Extraction of specific common genetic network of side effect pair, and prediction of side effects for a drug based on PPI network." Journal of the Korea Society of Computer and Information 21, no. 1 (2016): 115–23. http://dx.doi.org/10.9708/jksci.2016.21.1.115.

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Pauwels, Edouard, Véronique Stoven, and Yoshihiro Yamanishi. "Predicting drug side-effect profiles: a chemical fragment-based approach." BMC Bioinformatics 12, no. 1 (2011): 169. http://dx.doi.org/10.1186/1471-2105-12-169.

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Yu, Liyi, Meiling Cheng, Wangren Qiu, Xuan Xiao, and Weizhong Lin. "idse-HE: Hybrid embedding graph neural network for drug side effects prediction." Journal of Biomedical Informatics 131 (July 2022): 104098. http://dx.doi.org/10.1016/j.jbi.2022.104098.

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Lee, Chun Yen, and Yi‐Ping Phoebe Chen. "Descriptive prediction of drug side‐effects using a hybrid deep learning model." International Journal of Intelligent Systems 36, no. 6 (2021): 2491–510. http://dx.doi.org/10.1002/int.22389.

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Jahid, Md Jamiul, and Jianhua Ruan. "Structure-based prediction of drug side effects using a novel classification algorithm." International Journal of Computational Biology and Drug Design 9, no. 1/2 (2016): 87. http://dx.doi.org/10.1504/ijcbdd.2016.074985.

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CHEN, Y. Z., Z. R. LI, and C. Y. UNG. "COMPUTATIONAL METHOD FOR DRUG TARGET SEARCH AND APPLICATION IN DRUG DISCOVERY." Journal of Theoretical and Computational Chemistry 01, no. 01 (2002): 213–24. http://dx.doi.org/10.1142/s0219633602000166.

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Ligand-protein inverse docking has recently been introduced as a computer method for identification of potential protein targets of a drug. A protein structure database is searched to find proteins to which a drug can bind or weakly bind. Examples of potential applications of this method in facilitating drug discovery include: (1) identification of unknown and secondary therapeutic targets of a drug, (2) prediction of potential toxicity and side effect of an investigative drug, and (3) probing molecular mechanism of bioactive herbal compounds such as those extracted from plants used in traditi

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Islam, Sk Mazharul, Sk Md Mosaddek Hossain, and Sumanta Ray. "DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation." PLOS ONE 16, no. 2 (2021): e0246920. http://dx.doi.org/10.1371/journal.pone.0246920.

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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

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Sun, Yifan, Yi Xiong, Qian Xu, and Dongqing Wei. "A Hadoop-Based Method to Predict Potential Effective Drug Combination." BioMed Research International 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/196858.

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Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support v

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Xuan, Ping, Yangkun Cao, Tiangang Zhang, Xiao Wang, Shuxiang Pan, and Tonghui Shen. "Drug repositioning through integration of prior knowledge and projections of drugs and diseases." Bioinformatics 35, no. 20 (2019): 4108–19. http://dx.doi.org/10.1093/bioinformatics/btz182.

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Abstract Motivation Identifying and developing novel therapeutic effects for existing drugs contributes to reduction of drug development costs. Most of the previous methods focus on integration of the heterogeneous data of drugs and diseases from multiple sources for predicting the candidate drug–disease associations. However, they fail to take the prior knowledge of drugs and diseases and their sparse characteristic into account. It is essential to develop a method that exploits the more useful information to predict the reliable candidate associations. Results We present a method based on no

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Liang, Siqi, and Haiyuan Yu. "Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach." Bioinformatics 36, no. 16 (2020): 4490–97. http://dx.doi.org/10.1093/bioinformatics/btaa495.

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Abstract Motivation In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Results Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug–gene phenotype similarity and gene expression profile similarity that capture information orthog

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Jaundoo, Rajeev, and Travis J. A. Craddock. "DRUGPATH: A New Database for Mapping Polypharmacology." Alberta Academic Review 2, no. 3 (2019): 4. http://dx.doi.org/10.29173/aar92.

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While there are existing databases that curate only drug, target, or pathway data for instance, none of these alone are exhaustive. The Drug Gene Pathway (DRUGPATH) meta database was created as a response to the complex treatment required for various diseases including Gulf War Illness (GWI) and post-traumatic stress disorder (PTSD), where therapy involves using multiple drugs in combination. Here, drug-drug interactions can occur due to the promiscuous nature of pharmaceuticals, which can then lead to various side effects or can alternatively be utilized towards drug repurposing. The objectiv

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Sachdev, Kanica, and Manoj K. Gupta. "A comprehensive review of computational techniques for the prediction of drug side effects." Drug Development Research 81, no. 6 (2020): 650–70. http://dx.doi.org/10.1002/ddr.21669.

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Samizadeh, Mina, and Behrouz Minaei-Bidgoli. "Drug-target Interaction Prediction by Metapath2vec Node Embedding in Heterogeneous Network of Interactions." International Journal on Artificial Intelligence Tools 29, no. 01 (2020): 2050001. http://dx.doi.org/10.1142/s0218213020500013.

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Drug discovery is a complicated, time-consuming and expensive process. The cost for each new molecular entity (NME) is estimated at $1.8 billion. Furthermore, for a new drug to be FDA approved it often takes nearly a decade and approximately 20 new drugs being approved by the US Food and Drug Administration (FDA) each year. Accurately predicting drug-target interactions (DTIs) by computational methods is an important area of drug research, which brings about a broad prospect for fast and low-risk drug development. By accurate prediction of drugs and targets interactions scientists can scale-do

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Kulemina, Lidia V., and David A. Ostrov. "Prediction of Off-Target Effects on Angiotensin-Converting Enzyme 2." Journal of Biomolecular Screening 16, no. 8 (2011): 878–85. http://dx.doi.org/10.1177/1087057111413919.

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The authors describe a structure-based strategy to identify therapeutically beneficial off-target effects by screening a chemical library of Food and Drug Administration (FDA)–approved small-molecule drugs matching pharmacophores defined for specific target proteins. They applied this strategy to angiotensin-converting enzyme 2 (ACE2), an enzyme that generates vasodilatory peptides and promotes protection from hypertension-associated cardiovascular disease. The conformation-based structural selection method by molecular docking using DOCK allowed them to identify a series of FDA-approved drugs

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Wang, Meng, Haofen Wang, Xing Liu, Xinyu Ma, and Beilun Wang. "Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study." JMIR Medical Informatics 9, no. 6 (2021): e28277. http://dx.doi.org/10.2196/28277.

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Background Minimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discovering various DDIs. However, these data contain a huge amount of noise and provide knowledge bases that are far from being complete or used with reliability. Most existing studies focus on predicting binary DDIs between drug pairs and ignore other interactions. Objecti

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Gao, Yu-Fei, Lei Chen, Guo-Hua Huang, et al. "Prediction of Drugs Target Groups Based on ChEBI Ontology." BioMed Research International 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/132724.

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Most drugs have beneficial as well as adverse effects and exert their biological functions by adjusting and altering the functions of their target proteins. Thus, knowledge of drugs target proteins is essential for the improvement of therapeutic effects and mitigation of undesirable side effects. In the study, we proposed a novel prediction method based on drug/compound ontology information extracted from ChEBI to identify drugs target groups from which the kind of functions of a drug may be deduced. By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four

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B, Nithya, and Anitha G. "Drug Side-effects Prediction using Hierarchical Fuzzy Deep Learning for Diagnosing Specific Disease." International Journal of Engineering Trends and Technology 70, no. 8 (2022): 140–48. http://dx.doi.org/10.14445/22315381/ijett-v70i8p214.

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Zhao, Xian, Lei Chen, and Jing Lu. "A similarity-based method for prediction of drug side effects with heterogeneous information." Mathematical Biosciences 306 (December 2018): 136–44. http://dx.doi.org/10.1016/j.mbs.2018.09.010.

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Domingo-Fernández, Daniel, Yojana Gadiya, Abhishek Patel, et al. "Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery." PLOS Computational Biology 18, no. 2 (2022): e1009909. http://dx.doi.org/10.1371/journal.pcbi.1009909.

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Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given dis

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