Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions

毒性 环境毒理学 不良结局途径 生化工程 计算机科学 环境化学 计算生物学 化学 生物 工程类 有机化学
作者
Liang Meng,Haibo Li,Haijun Liu,Yuefang Chen,Rongfang Yuan,Zhongbing Chen,Shuai Luo,Huilun Chen
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:946: 174201-174201
标识
DOI:10.1016/j.scitotenv.2024.174201
摘要

Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
充电宝应助ref:rain采纳,获得50
刚刚
1秒前
2秒前
3秒前
5秒前
5秒前
壹yi完成签到,获得积分10
5秒前
西西发布了新的文献求助10
6秒前
852应助Jun采纳,获得10
7秒前
7秒前
7秒前
今后应助邓艳梅采纳,获得10
7秒前
7秒前
Puan完成签到,获得积分10
8秒前
8秒前
8秒前
奋斗雁枫发布了新的文献求助10
10秒前
10秒前
10秒前
香蕉觅云应助清新的冷荷采纳,获得10
11秒前
日暮炊烟发布了新的文献求助10
11秒前
内向绿竹发布了新的文献求助10
12秒前
12秒前
Puan发布了新的文献求助10
13秒前
风中采枫完成签到,获得积分10
14秒前
rrrrrrry发布了新的文献求助30
14秒前
甜甜玫瑰应助奔跑的小鹰采纳,获得10
15秒前
小月986发布了新的文献求助10
15秒前
16秒前
haku发布了新的文献求助10
17秒前
19秒前
武雨寒发布了新的文献求助10
19秒前
守望阳光1完成签到,获得积分10
20秒前
21秒前
21秒前
搜集达人应助chili采纳,获得10
22秒前
科研通AI2S应助炒栗子采纳,获得10
22秒前
22秒前
楚辞发布了新的文献求助30
22秒前
未来可期发布了新的文献求助10
23秒前
高分求助中
Sustainability in ’Tides Chemistry 2000
Studien zur Ideengeschichte der Gesetzgebung 1000
The ACS Guide to Scholarly Communication 1000
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
Handbook of the Mammals of the World – Volume 3: Primates 805
Ethnicities: Media, Health, and Coping 800
Gerard de Lairesse : an artist between stage and studio 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3072205
求助须知:如何正确求助?哪些是违规求助? 2726027
关于积分的说明 7492250
捐赠科研通 2373536
什么是DOI,文献DOI怎么找? 1258633
科研通“疑难数据库(出版商)”最低求助积分说明 610333
版权声明 596952