Challenges with the application and adoption of artificial intelligence for drug discovery

Ghita Ghislat, Saiveth Hernandez-Hernandez, Chayanit Piwajanusorn, Pedro J. Ballester
{"title":"Challenges with the application and adoption of artificial intelligence for drug discovery","authors":"Ghita Ghislat, Saiveth Hernandez-Hernandez, Chayanit Piwajanusorn, Pedro J. Ballester","doi":"arxiv-2407.05150","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI) is exhibiting tremendous potential to reduce the\nmassive costs and long timescales of drug discovery. There are however\nimportant challenges limiting the impact and scope of AI models. Typically,\nthese models are evaluated on benchmarks that are unlikely to anticipate their\nprospective performance, which inadvertently misguides their development.\nIndeed, while all the developed models excel in a selected benchmark, only a\nsmall proportion of them are ultimately reported to have prospective value\n(e.g. by discovering potent and innovative drug leads for a therapeutic\ntarget). Here we discuss a range of data issues (bias, inconsistency, skewness,\nirrelevance, small size, high dimensionality), how they challenge AI models and\nwhich issue-specific mitigations have been effective. Next, we point out the\nchallenges faced by uncertainty quantification techniques aimed at enhancing\nthese AI models. We also discuss how conceptual errors, unrealistic benchmarks\nand performance misestimation can confound the evaluation of models and thus\ntheir development. Lastly, we explain how human bias, whether from AI experts\nor drug discovery experts, constitutes another challenge that can be alleviated\nwith prospective studies.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"23 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.05150","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

Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges limiting the impact and scope of AI models. Typically, these models are evaluated on benchmarks that are unlikely to anticipate their prospective performance, which inadvertently misguides their development. Indeed, while all the developed models excel in a selected benchmark, only a small proportion of them are ultimately reported to have prospective value (e.g. by discovering potent and innovative drug leads for a therapeutic target). Here we discuss a range of data issues (bias, inconsistency, skewness, irrelevance, small size, high dimensionality), how they challenge AI models and which issue-specific mitigations have been effective. Next, we point out the challenges faced by uncertainty quantification techniques aimed at enhancing these AI models. We also discuss how conceptual errors, unrealistic benchmarks and performance misestimation can confound the evaluation of models and thus their development. Lastly, we explain how human bias, whether from AI experts or drug discovery experts, constitutes another challenge that can be alleviated with prospective studies.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
在药物发现中应用和采用人工智能面临的挑战
人工智能(AI)在降低药物发现的巨额成本和缩短时间尺度方面展现出巨大潜力。然而,人工智能模型的影响和范围受到一些重要挑战的限制。事实上,虽然所有开发的模型都在选定的基准中表现出色,但只有一小部分模型最终被报告为具有前瞻性价值(例如为治疗目标发现了强效创新药物线索)。在此,我们将讨论一系列数据问题(偏差、不一致性、倾斜度、不相关性、小规模、高维度),它们如何对人工智能模型构成挑战,以及哪些针对特定问题的缓解措施是有效的。接下来,我们指出了旨在增强这些人工智能模型的不确定性量化技术所面临的挑战。我们还讨论了概念性错误、不切实际的基准和性能错误估计会如何扰乱模型评估和模型开发。最后,我们解释了人类偏见(无论是来自人工智能专家还是药物发现专家)是如何构成另一个挑战的,而这可以通过前瞻性研究来缓解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Opportunities and challenges of mRNA technologies in development of Dengue Virus Vaccine Compatibility studies of loquat scions with loquat and quince rootstocks Analysis of Potential Biases and Validity of Studies Using Multiverse Approaches to Assess the Impacts of Government Responses to Epidemics Advances in Nanoparticle-Based Targeted Drug Delivery Systems for Colorectal Cancer Therapy: A Review Unveiling Parkinson's Disease-like Changes Triggered by Spaceflight
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1