{"title":"Harnessing Artificial Intelligence in Drug Discovery: Transformative Approaches and Future Directions.","authors":"Damini Dilip Salunke, Sunil Thitame, Ashwini Aher","doi":"10.4103/jpbs.jpbs_1770_24","DOIUrl":null,"url":null,"abstract":"<p><p>The most strategic weapon in drug discovery in the recent past has been artificial intelligence (AI)-bringing new approaches to one of the toughest areas of the pharmaceutical industry. Various AI approaches such as DL and ML methods utilized in various stages of drug discovery and development including but not limited to virtual screening and target identification are also discussed here. Employing this approach, this review looks at AI programs and platforms that exist in drug discovery today in a bid to outline what a future with AI in this field has in stock. In addition to this, this review does not only give a momentary state of the state of affairs of the AI in the space, but also briefly discusses what is in store next, along with the drawback and the opportunity more so from this perspective.</p>","PeriodicalId":94339,"journal":{"name":"Journal of pharmacy & bioallied sciences","volume":"17 Suppl 1","pages":"S52-S54"},"PeriodicalIF":0.7000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12156605/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of pharmacy & bioallied sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4103/jpbs.jpbs_1770_24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/15 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
The most strategic weapon in drug discovery in the recent past has been artificial intelligence (AI)-bringing new approaches to one of the toughest areas of the pharmaceutical industry. Various AI approaches such as DL and ML methods utilized in various stages of drug discovery and development including but not limited to virtual screening and target identification are also discussed here. Employing this approach, this review looks at AI programs and platforms that exist in drug discovery today in a bid to outline what a future with AI in this field has in stock. In addition to this, this review does not only give a momentary state of the state of affairs of the AI in the space, but also briefly discusses what is in store next, along with the drawback and the opportunity more so from this perspective.
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