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
hh发布了新的文献求助10
1秒前
羊羊完成签到,获得积分10
1秒前
卡卡罗特先森完成签到 ,获得积分10
2秒前
朽木完成签到 ,获得积分10
4秒前
4秒前
fanyueyue应助111采纳,获得10
6秒前
6秒前
6秒前
kcmat发布了新的文献求助10
7秒前
hh完成签到,获得积分10
8秒前
Philadelphus发布了新的文献求助10
9秒前
einuo完成签到,获得积分10
9秒前
AKYDXS完成签到,获得积分10
12秒前
昏睡的蟠桃应助Llllll采纳,获得200
12秒前
科研通AI2S应助hao采纳,获得10
12秒前
13秒前
13秒前
香蕉觅云应助阿湫采纳,获得10
14秒前
星辰大海应助星辰采纳,获得10
14秒前
阿卡宁完成签到,获得积分10
14秒前
lzw完成签到 ,获得积分10
14秒前
沉静烧仙草完成签到,获得积分20
15秒前
烟花应助嘉嘉琦采纳,获得10
15秒前
隐形曼青应助科研通管家采纳,获得10
15秒前
Hello应助科研通管家采纳,获得10
16秒前
Ava应助科研通管家采纳,获得10
16秒前
上官若男应助科研通管家采纳,获得10
16秒前
16秒前
赘婿应助科研通管家采纳,获得10
16秒前
烟花应助科研通管家采纳,获得10
16秒前
FashionBoy应助科研通管家采纳,获得10
16秒前
英俊的铭应助科研通管家采纳,获得10
16秒前
在水一方应助科研通管家采纳,获得10
16秒前
accepted应助科研通管家采纳,获得10
16秒前
脑洞疼应助科研通管家采纳,获得10
16秒前
16秒前
cdh1994应助kcmat采纳,获得10
16秒前
我是老大应助科研通管家采纳,获得10
16秒前
乐乐应助科研通管家采纳,获得10
16秒前
FashionBoy应助科研通管家采纳,获得10
16秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 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 4038524
求助须知:如何正确求助?哪些是违规求助? 3576221
关于积分的说明 11374737
捐赠科研通 3305912
什么是DOI,文献DOI怎么找? 1819354
邀请新用户注册赠送积分活动 892688
科研通“疑难数据库(出版商)”最低求助积分说明 815048