GSL-DTI: Graph structure learning network for Drug-Target interaction prediction

药物发现 构造(python库) 计算机科学 药物靶点 人工智能 机器学习 图形 生物 理论计算机科学 生物信息学 化学 生物化学 程序设计语言
作者
E Zixuan,Guanyu Qiao,Guohua Wang,Yang Li
出处
期刊:Methods [Elsevier BV]
卷期号:223: 136-145 被引量:6
标识
DOI:10.1016/j.ymeth.2024.01.018
摘要

Motivation: Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins. We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta-paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction.
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
JS发布了新的文献求助10
刚刚
烟花应助学术裁缝采纳,获得10
刚刚
1秒前
EasonYan发布了新的文献求助10
1秒前
dypdyp应助ZH采纳,获得10
1秒前
东日发布了新的文献求助10
1秒前
Leilei完成签到,获得积分20
1秒前
呆萌代桃发布了新的文献求助10
1秒前
万能图书馆应助yan123采纳,获得10
2秒前
小二郎应助俊逸的代曼采纳,获得10
2秒前
3秒前
呼呼哈哈完成签到,获得积分10
3秒前
D5发布了新的文献求助10
3秒前
勤奋的绪发布了新的文献求助10
4秒前
xiaxue发布了新的文献求助10
5秒前
5秒前
mojinzhao完成签到,获得积分10
6秒前
明天见完成签到,获得积分10
6秒前
TMOMOR应助小赵采纳,获得10
7秒前
hh完成签到 ,获得积分10
8秒前
CipherSage应助ZHQ采纳,获得10
8秒前
我不是很帅完成签到,获得积分10
8秒前
9秒前
JosephLee发布了新的文献求助10
9秒前
9秒前
9秒前
华西招生版完成签到,获得积分10
10秒前
10秒前
xxxxfiona发布了新的文献求助10
10秒前
12秒前
12秒前
情殇发布了新的文献求助10
12秒前
陈梦发布了新的文献求助10
13秒前
Elsia完成签到 ,获得积分10
13秒前
MchemG应助apeng采纳,获得10
14秒前
cora发布了新的文献求助10
14秒前
学术宝马完成签到,获得积分20
14秒前
wanci应助糟糕的道罡采纳,获得10
14秒前
15秒前
乐观小之应助绿豆蛙采纳,获得10
15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
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 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 3969458
求助须知:如何正确求助?哪些是违规求助? 3514286
关于积分的说明 11173363
捐赠科研通 3249652
什么是DOI,文献DOI怎么找? 1794948
邀请新用户注册赠送积分活动 875501
科研通“疑难数据库(出版商)”最低求助积分说明 804836