Hierarchical Clustering Split for Low-Bias Evaluation of Drug-Target Interaction Prediction

概化理论 计算机科学 聚类分析 机器学习 人工智能 随机森林 数据挖掘 深度学习 统计 数学
作者
Peizhen Bai,Filip Miljković,Yan Ge,Nigel Greene,Bino John,Haiping Lu
标识
DOI:10.1109/bibm52615.2021.9669515
摘要

Drug-target interaction (DTI) prediction is important in drug discovery and chemogenomics studies. Machine learning, particularly deep learning, has advanced this area significantly over the past few years. However, a significant gap between the performance reported in academic papers and that in practical drug discovery settings, e.g. the random-split-based evaluation strategy tends to be too optimistic in estimating the prediction performance in real-world settings. Such performance gap is largely due to hidden data bias in experimental datasets and inappropriate data split. In this paper, we construct a low-bias DTI dataset and study more challenging data split strategies to improve performance evaluation for real-world settings. Specifically, we study the data bias in a popular DTI dataset, BindingDB, and re-evaluate the prediction performance of three state-of-the-art deep learning models using five different data split strategies: random split, cold drug split, scaffold split, and two hierarchical-clustering-based splits. In addition, we comprehensively examine six performance metrics. Our experimental results confirm the overoptimism of the popular random split and show that hierarchical-clustering-based splits are far more challenging and can provide potentially more useful assessment of model generalizability in real-world DTI prediction settings.
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
PDF的下载单位、IP信息已删除 (2025-6-4)

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
xiuxue424完成签到,获得积分10
刚刚
脑洞疼应助Giner采纳,获得10
刚刚
热情的果汁完成签到,获得积分10
1秒前
华仔应助King采纳,获得30
1秒前
1秒前
1秒前
1秒前
fy207发布了新的文献求助10
1秒前
归尘发布了新的文献求助10
2秒前
2秒前
manaka1225完成签到,获得积分10
2秒前
2秒前
可爱的函函应助nxdsk采纳,获得10
2秒前
落林樾完成签到,获得积分10
2秒前
3秒前
帅气的猫发布了新的文献求助10
3秒前
3秒前
4秒前
祝笑柳完成签到,获得积分10
4秒前
xuxu发布了新的文献求助10
4秒前
4秒前
lyy完成签到 ,获得积分10
5秒前
华仔应助Mr_X采纳,获得10
5秒前
5秒前
落林樾发布了新的文献求助10
6秒前
归尘完成签到,获得积分10
6秒前
超帅的元柏完成签到,获得积分10
7秒前
宛海发布了新的文献求助10
7秒前
lalala发布了新的文献求助10
7秒前
是我呀小夏完成签到 ,获得积分10
8秒前
8秒前
汪汪队发布了新的文献求助10
8秒前
8秒前
8秒前
8秒前
8秒前
zyt完成签到,获得积分10
9秒前
彭于彦祖应助活泼的南风采纳,获得30
9秒前
9秒前
共享精神应助瘦瘦的鬼神采纳,获得30
10秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 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 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969322
求助须知:如何正确求助?哪些是违规求助? 3514152
关于积分的说明 11172188
捐赠科研通 3249407
什么是DOI,文献DOI怎么找? 1794832
邀请新用户注册赠送积分活动 875437
科研通“疑难数据库(出版商)”最低求助积分说明 804781