[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128159-en":3,"doc-seo-128159-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128159,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Drug Target Interaction Prediction Using Machine Learning Techniques - A Review","Drug discovery is a time- and cost-intensive process driven by the need for effective medication throughout life. Drugs act by inhibiting or activating protein functions, so successful drug design relies on drug targets and their binding interactions. This review focuses on drug-target interaction (DTI) prediction using machine learning within computer-aided drug design, including the role of protein pockets and binding-site identification. It summarizes qualitative and quantitative results from prior classifiers, notes dataset limitations, and highlights the need for improved negative pairs and better DTI classifiers.","International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 8, Nº6  \nDrug Target Interaction Prediction Using Machine Learning Techniques – A Review  \nA. Suruliandi1, T. Idhaya1, S. P. Raja2 *  \n1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Abhishekapatti, Tirunelveli,  \nTamilNadu (India)  \n2 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, TamilNadu (India)  \n* [Corresponding author.](Corresponding author. suruliandi@yahoo.com)[ suruliandi@yahoo.com](Corresponding author. suruliandi@yahoo.com) (A. Suruliandi), [idhayathomas003@gmail.com](idhayathomas003@gmail.com) (T. Idhaya), [avemariaraja@gmail.com](avemariaraja@gmail.com) (S. P. Raja).  \nReceived 13 August 2021 | Accepted 4 January 2022 | Early Access 10 November 2022  \nAbstract   \nDrug discovery is a key process, given the rising and ubiquitous demand for medication to stay in good shape right through the course of one’s life. Drugs are small molecules that inhibit or activate the function of a protein, offering patients a host of therapeutic benefits. Drug design is the inventive process of finding new medication, based on targets or proteins. Identifying new drugs is a process that involves time and money. This is where computer-aided drug design helps cut time and costs. Drug design needs drug targets that are a protein and a drug compound, with which the interaction between a drug and a target is established. Interaction, in this context, refers to the process of discovering protein binding sites, which are protein pockets that bind with drugs. Pockets are regions on a protein macromolecule that bind to drug molecules. Researchers have been at work trying to determine new Drug Target Interactions (DTI) that predict whether or not a given drug molecule will bind to a target. Machine learning (ML) techniques help establish the interaction between drugs and their targets, using computer-aided drug design. This paper aims to explore ML techniques better for DTI prediction and boost future research. Qualitative and quantitative analyses of ML techniques show that several have been applied to predict DTIs, employing a range of classifiers. Though DTI prediction improves with negative drug target pairs (DTP), the lack of true negative DTPs has led to the use a particular dataset of drugs and targets. Using dynamic DTPs improves DTI prediction. Little attention has so far been paid to developing a new classifier for DTI classification, and there is, unquestionably, a need for better ones.  \nKeywords  \nChemogenomics, Drug Databases, Drug Discovery, Drug Target Interactions, Machine Learning, Targets, Target Databases.  \nDOI: 10. 9781/ijimai.2022.11.002  \nI. Introduction  \nDiscovering new  \nfor medication in  \ndrugs is critical and driven by the need daily life, partly brought on by changing  \nenvironmental conditions. Nevertheless, drug discovery is not easy, it demands time as well as money, and the drug success rate is usually low. Computer-Aided Drug Design (CADD) is considered a computational discipline that aims to discover, design, and develop therapeutic chemical targets. There are 3 phases in drug design discovery, development, and registry.  \nIn the first phase, discovery, the focus is on identifying a new drug and its targets, based on binding sites. The second phase, development, involves pre-clinical research, where the drug is tested on animals for safety. Successful research means that human trials are set in motion. In the third phase, registry, the Food and Drug Administration (FDA) thoroughly reviews all the submitted drug-related data and decides on its approval or otherwise. Initiating an efficient computational model that finds potential Drug Target Interaction (DTI) from biological data  \nhelps understand the biological process, recognize novel drugs, and offer improved therapeutic medicine for illnesses of all sorts. Drug development has three trial phases, e","cbCaiuZKS43MoIhd","https://ap.wps.com/l/cbCaiuZKS43MoIhd","pdf",2502996,3,1,15,"English","en",105,"# Introduction\n## Computer-Aided Drug Design and DTI prediction\n## Drug discovery phases and challenges\n# State of the Art Methods\n## DTI discovery approaches\n## Ligand-based, docking-based, and chemogenomics-based methods","[{\"question\":\"What is drug-target interaction (DTI) prediction and why is it important?\",\"answer\":\"DTI prediction aims to determine whether a given drug molecule binds to a target protein. It supports computer-aided drug design by helping identify potential interactions from biological data.\"},{\"question\":\"Which machine learning approaches are used for DTI prediction?\",\"answer\":\"The review describes that multiple ML techniques and classifiers have been applied to predict DTIs. It discusses both qualitative and quantitative analyses reported in prior work.\"},{\"question\":\"What challenge does the paper highlight about true negative drug-target pairs?\",\"answer\":\"The paper notes the lack of true negative drug-target pairs leads researchers to rely on specific datasets. It also states that using dynamic negative pairs can improve DTI prediction.\"}]","Drug Target Interaction Prediction Using Machine Learning Techniques - A Review | PDF",1785945192,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"drug-target-interaction-prediction-using-machine-learning-techniques-a-review","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/drug-target-interaction-prediction-using-machine-learning-techniques-a-review/128159/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is drug-target interaction (DTI) prediction and why is it important?","Question",{"text":76,"@type":77},"DTI prediction aims to determine whether a given drug molecule binds to a target protein. It supports computer-aided drug design by helping identify potential interactions from biological data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning approaches are used for DTI prediction?",{"text":81,"@type":77},"The review describes that multiple ML techniques and classifiers have been applied to predict DTIs. It discusses both qualitative and quantitative analyses reported in prior work.",{"name":83,"@type":74,"acceptedAnswer":84},"What challenge does the paper highlight about true negative drug-target pairs?",{"text":85,"@type":77},"The paper notes the lack of true negative drug-target pairs leads researchers to rely on specific datasets. It also states that using dynamic negative pairs can improve DTI prediction.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]