Pub Date : 2024-03-21DOI: 10.2174/0118722083297406240313090140
P. Singh, Kapil Sachan, Vishal Khandelwal, Sumita Singh, Smita Singh
Traditional drug discovery methods such as wet-lab testing, validations, and synthetic techniques are time-consuming and expensive. Artificial Intelligence (AI) approaches have progressed to the point where they can have a significant impact on the drug discovery process. Using massive volumes of open data, artificial intelligence methods are revolutionizing the pharmaceutical industry. In the last few decades, many AI-based models have been developed and implemented in many areas of the drug development process. These models have been used as a supplement to conventional research to uncover superior pharmaceuticals expeditiously. Drug research and development to repurposing and productivity benefits in the pharmaceutical business through clinical trials. AI is studied in this article for its numerous potential uses. We have discussed how AI can be put to use in the pharmaceutical sector, specifically for predicting a drug's toxicity, bioactivity, and physicochemical characteristics, among other things. In this review article, we have discussed its application to a variety of problems, including de novo drug discovery, target structure prediction, interaction prediction, and binding affinity prediction. AI for predicting drug interactions and nanomedicines were also considered.
{"title":"Role of Artificial Intelligence in Drug Discovery to Revolutionize\u0000the Pharmaceutical Industry: Resources, Methods and Applications","authors":"P. Singh, Kapil Sachan, Vishal Khandelwal, Sumita Singh, Smita Singh","doi":"10.2174/0118722083297406240313090140","DOIUrl":"https://doi.org/10.2174/0118722083297406240313090140","url":null,"abstract":"\u0000\u0000Traditional drug discovery methods such as wet-lab testing, validations, and\u0000synthetic techniques are time-consuming and expensive. Artificial Intelligence (AI) approaches\u0000have progressed to the point where they can have a significant impact on the\u0000drug discovery process. Using massive volumes of open data, artificial intelligence\u0000methods are revolutionizing the pharmaceutical industry. In the last few decades, many\u0000AI-based models have been developed and implemented in many areas of the drug development\u0000process. These models have been used as a supplement to conventional research\u0000to uncover superior pharmaceuticals expeditiously. Drug research and development\u0000to repurposing and productivity benefits in the pharmaceutical business through\u0000clinical trials. AI is studied in this article for its numerous potential uses. We have discussed\u0000how AI can be put to use in the pharmaceutical sector, specifically for predicting a\u0000drug's toxicity, bioactivity, and physicochemical characteristics, among other things. In\u0000this review article, we have discussed its application to a variety of problems, including\u0000de novo drug discovery, target structure prediction, interaction prediction, and binding affinity\u0000prediction. AI for predicting drug interactions and nanomedicines were also considered.\u0000","PeriodicalId":21064,"journal":{"name":"Recent patents on biotechnology","volume":" 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}