{"title":"AI-powered drug discovery for neglected diseases: accelerating public health solutions in the developing world.","authors":"M D Nahid Hassan Nishan","doi":"10.7189/jogh.15.03002","DOIUrl":null,"url":null,"abstract":"<p><p>The emergence of artificial intelligence (AI) in drug discovery represents a transformative development in addressing neglected diseases, particularly in the context of the developing world. Neglected diseases, often overlooked by traditional pharmaceutical research due to limited commercial profitability, pose significant public health challenges in low- and middle-income countries. AI-powered drug discovery offers a promising solution by accelerating the identification of potential drug candidates, optimising the drug development process, and reducing the time and cost associated with bringing new treatments to market. However, while AI shows promise, many of its applications are still in their early stages and require human validation to ensure the accuracy and reliability of predictions. Additionally, AI models are limited by the availability of high-quality data, which is often sparse in regions where neglected diseases are most prevalent. This viewpoint explores the application of AI in drug discovery for neglected diseases, examining its current impact, related ethical considerations, and the broader implications for public health in the developing world. It also highlights the challenges and opportunities presented by AI in this context, emphasising the need for ongoing research, ethical oversight, and collaboration between public health stakeholders to fully realise its potential in transforming global health outcomes.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"03002"},"PeriodicalIF":4.5000,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Global Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.7189/jogh.15.03002","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
The emergence of artificial intelligence (AI) in drug discovery represents a transformative development in addressing neglected diseases, particularly in the context of the developing world. Neglected diseases, often overlooked by traditional pharmaceutical research due to limited commercial profitability, pose significant public health challenges in low- and middle-income countries. AI-powered drug discovery offers a promising solution by accelerating the identification of potential drug candidates, optimising the drug development process, and reducing the time and cost associated with bringing new treatments to market. However, while AI shows promise, many of its applications are still in their early stages and require human validation to ensure the accuracy and reliability of predictions. Additionally, AI models are limited by the availability of high-quality data, which is often sparse in regions where neglected diseases are most prevalent. This viewpoint explores the application of AI in drug discovery for neglected diseases, examining its current impact, related ethical considerations, and the broader implications for public health in the developing world. It also highlights the challenges and opportunities presented by AI in this context, emphasising the need for ongoing research, ethical oversight, and collaboration between public health stakeholders to fully realise its potential in transforming global health outcomes.
期刊介绍:
Journal of Global Health is a peer-reviewed journal published by the Edinburgh University Global Health Society, a not-for-profit organization registered in the UK. We publish editorials, news, viewpoints, original research and review articles in two issues per year.