ComNet: A Multiview Deep Learning Model for Predicting Drug Combination Side Effects

人工智能 副作用(计算机科学) 计算机科学 深度学习 药品 机器学习 医学 药理学 程序设计语言
作者
Zuolong Zhang,Zhiyuan Liu,Xu Shang,Shengbo Chen,Fang Zuo,Yi Wu,Dazhi Long
出处
期刊:Journal of Chemical Information and Modeling [American Chemical Society]
标识
DOI:10.1021/acs.jcim.4c01737
摘要

As combination therapy becomes more common in clinical applications, predicting adverse effects of combination medications is a challenging task. However, there are three limitations of the existing prediction models. First, they rely on a single view of the drug and cannot fully utilize multiview information, resulting in limited performance when capturing complex structures. Second, they ignore subgraph information at different scales, which limits the ability to model interactions between subgraphs. Third, there has been limited research on effectively integrating multiview features of molecules. Therefore, we propose ComNet, a deep learning model that improves the accuracy of side effect prediction by integrating multiview features of drugs. First, to capture diverse features of drugs, a multiview feature extraction module is proposed, which not only uses molecular fingerprints but also extracts semantic information on SMILES and spatial information on 3D conformations. Second, to enhance the modeling ability of complex structures, a multiscale subgraph fusion mechanism is proposed, which can fuse local and global graph structures of drugs. Finally, a multiview feature fusion mechanism is proposed, which uses an attention mechanism to adaptively adjust the weights of different views to achieve multiview data fusion. Experiments on several publicly available data sets show that ComNet performs better than existing methods in various complex scenarios, especially in cold-start scenarios. Ablation experiments show that each core structure in ComNet contributes to the overall performance. Further analysis shows that ComNet not only converges rapidly and has good generalization ability but also identifies different substructures in the molecule. Finally, a case study on a self-collected data set validates the superior performance of ComNet in practical applications.
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
gsj完成签到,获得积分10
2秒前
3秒前
4秒前
秋天完成签到,获得积分10
5秒前
淡定的惜关注了科研通微信公众号
8秒前
Zhy发布了新的文献求助10
8秒前
9秒前
蛋挞完成签到,获得积分10
11秒前
李健应助Junning采纳,获得10
13秒前
13秒前
tingting9发布了新的文献求助10
14秒前
15秒前
爆米花应助mark采纳,获得10
16秒前
16秒前
爱看文献的小恐龙完成签到,获得积分10
17秒前
小饼干完成签到 ,获得积分10
19秒前
萧瑟处完成签到 ,获得积分10
20秒前
20秒前
21秒前
孙燕应助鹤鸣采纳,获得30
22秒前
23秒前
loski发布了新的文献求助10
25秒前
随便不放假完成签到 ,获得积分10
26秒前
淡定的惜发布了新的文献求助10
28秒前
威武的亦绿完成签到,获得积分10
28秒前
木丁完成签到,获得积分10
29秒前
29秒前
32秒前
量子星尘发布了新的文献求助30
34秒前
孙燕应助成就山河采纳,获得10
35秒前
36秒前
领导范儿应助幸福大白采纳,获得30
37秒前
谓风发布了新的文献求助10
37秒前
38秒前
q1356478314应助天白采纳,获得10
38秒前
Hanni完成签到 ,获得积分10
38秒前
39秒前
yixiaolou发布了新的文献求助10
39秒前
Junning发布了新的文献求助10
40秒前
41秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3989406
求助须知:如何正确求助?哪些是违规求助? 3531522
关于积分的说明 11254187
捐赠科研通 3270174
什么是DOI,文献DOI怎么找? 1804901
邀请新用户注册赠送积分活动 882105
科研通“疑难数据库(出版商)”最低求助积分说明 809174