Predicting Drug Targets in Human Cancers.
Introduction: A large amount of knowledge has accumulated regarding the pharmacology of most cancer types, including their side effects, and during the last decade, many novel agents have emerged, accompanied by increased costs for healthcare systems. Aim: Artificial intelligence (AI) was applied in...
| Publicado en: | Journal of Clinical Pharmacy & Therapeutics Vol. 2025; pp. 1 - 29 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/26/2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189588329&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189588329 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02694727 EV4 jtl: Journal of Clinical Pharmacy & Therapeutics issn: 02694727 maglogo: Y pubinfo: dt: 11/26/2025 vid: 2025 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 189588329 189588329 189588329 10.1155/jcpt/4853636 189588329 ppf: 1 ppct: 28 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Predicting Drug Targets in Human Cancers. aug: au: Guzmán, David Calderón Brizuela, Norma Osnaya Peraza, Armando Valenzuela Herrera, Maribel Ortiz Garcia, Diego E. Ortega Olguin, Hugo Juarez Mejía, Gerardo Barragán Hasnain, Md Saquib affil: Neuroscience Laboratory,, National Paediatric Institute,, Ministry of Health,, Mexico City, Mexico, moh.koshi.gov.np sug: subj: Neoplasms Drug Therapy Antineoplastic Agents Therapeutic Use Artificial Intelligence Small Molecules Prediction Models Biological Factors United States Cancer Patients Computer-Aided Design Drug Design Drug Discovery Carrier Proteins Predictive Value of Tests Prediction Algorithms Drug Compounding Health Care Costs Chemotherapy, Cancer ab: Introduction: A large amount of knowledge has accumulated regarding the pharmacology of most cancer types, including their side effects, and during the last decade, many novel agents have emerged, accompanied by increased costs for healthcare systems. Aim: Artificial intelligence (AI) was applied in healthcare services, and the benefit of its use in the prediction of the targets of low molecular weight compounds that can affect biological processes was assessed (proportion of the top predicted targets, common name, target class and probability). Method: Designing the chemical agent or molecule chosen and the program will give the protein target prediction immediately by AI. A tuning algorithm device, with new information input in the web application interface, was used. Results: Information was arranged in order and fed to an in silico target prediction/Swiss TargetPrediction (STP). This tool is a computational method used to predict the most probable protein targets of small molecules, as well as to envisage what happens when many chemotherapy drugs are combined in cancer treatment. It aids in understanding some molecular processes controlling specific physical traits or biological activities to elucidate probable beneficial and adverse consequences. In addition, it helps to predict imprecision of defined molecules and pave the way to drug repositioning. Prediction is carried out employing the ligand‐based drug design (LBDD), which enables comparison of the correlation between the molecule under examination and the known binding molecules of a large set of target proteins. Conclusion: Target activity profiles enable a systematic repurposing process by extending the target profile of drugs, and then drug repurposing with AI provides a cost‐effective strategy to reuse approved drugs for new medical indications and the availability of new treatments, reduces development costs and mitigates risks due to the cost of the cancer drug agents, coupled with high cancer disease prevalence, which means the current cost trajectory is untenable for most world healthcare systems. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|