Xi Long, Barbara Steurer, Chun Wai Wong, Ekaterina Kozlova, Vladimir Naumov, F. Pun, Alex Aliper, Fengzhi Ren, Alex Zhavoronkov
{"title":"AI-enabled cancer target prioritization with optimal profiles balancing novelty, confidence and commercial tractability","authors":"Xi Long, Barbara Steurer, Chun Wai Wong, Ekaterina Kozlova, Vladimir Naumov, F. Pun, Alex Aliper, Fengzhi Ren, Alex Zhavoronkov","doi":"10.2217/fmai-2023-0019","DOIUrl":null,"url":null,"abstract":"The identification of new biological targets is crucial to advance cancer therapy, but deciphering the fiendishly complex processes that drive and sustain disease can be tedious and resource intensive. To optimize and accelerate the drug discovery process, artificial intelligence (AI) platforms are emerging that enable fast and cost-effective identification and prioritization of novel and disease-specific therapeutic targets with optimal target profiles, balancing confidence, novelty and commercial tractability. AI-streamlined target profiling has the potential to significantly improve the commercial burden of traditional drug development, and provides an unbiased approach for novel target identification. Here, we discuss the AI-assessed target profile and clinical relevance of genes recently identified by our AI-driven target discovery platform as top priority cancer targets.","PeriodicalId":154874,"journal":{"name":"Future Medicine AI","volume":"40 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Medicine AI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2217/fmai-2023-0019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The identification of new biological targets is crucial to advance cancer therapy, but deciphering the fiendishly complex processes that drive and sustain disease can be tedious and resource intensive. To optimize and accelerate the drug discovery process, artificial intelligence (AI) platforms are emerging that enable fast and cost-effective identification and prioritization of novel and disease-specific therapeutic targets with optimal target profiles, balancing confidence, novelty and commercial tractability. AI-streamlined target profiling has the potential to significantly improve the commercial burden of traditional drug development, and provides an unbiased approach for novel target identification. Here, we discuss the AI-assessed target profile and clinical relevance of genes recently identified by our AI-driven target discovery platform as top priority cancer targets.