Prediction of adverse drug reactions using drug convolutional neural networks

药物警戒 药物反应 计算机科学 卷积神经网络 化学信息学 药品 机器学习 生物信息学 过程(计算) 人工智能 药物不良反应 人工神经网络 药物发现 数量结构-活动关系 数据挖掘 医学 药理学 生物信息学 化学 基因 操作系统 生物 生物化学
作者
Anjani Sankar Mantripragada,Sai Phani Teja,Rohith Reddy Katasani,Pratik Joshi,V. Masilamani,Raj Ramesh
出处
期刊:Journal of Bioinformatics and Computational Biology [Imperial College Press]
卷期号:19 (01): 2050046-2050046 被引量:9
标识
DOI:10.1142/s0219720020500468
摘要

Prediction of Adverse Drug Reactions (ADRs) has been an important aspect of Pharmacovigilance because of its impact in the pharma industry. The standard process of introduction of a new drug into a market involves a lot of clinical trials and tests. This is a tedious and time consuming process and also involves a lot of monetary resources. The faster approval of a drug helps the patients who are in need of the drug. The in silico prediction of Adverse Drug Reactions can help speed up the aforementioned process. The challenges involved are lack of negative data present and predicting ADR from just the chemical structure. Although many models are already available to predict ADR, most of the models use biological activities identifiers, chemical and physical properties in addition to chemical structures of the drugs. But for most of the new drugs to be tested, only chemical structures will be available. The performance of the existing models predicting ADR only using chemical structures is not efficient. Therefore, an efficient prediction of ADRs from just the chemical structure has been proposed in this paper. The proposed method involves a separate model for each ADR, making it a binary classification problem. This paper presents a novel CNN model called Drug Convolutional Neural Network (DCNN) to predict ADRs using chemical structures of the drugs. The performance is measured using the metrics such as Accuracy, Recall, Precision, Specificity, F1 score, AUROC and MCC. The results obtained by the proposed DCNN model outperform the competing models on the SIDER4.1 database in terms of all the metrics. A case study has been performed on a COVID-19 recommended drugs, where the proposed model predicted the ADRs that are well aligned with the observations made by medical professionals using conventional methods.
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
mufcyang完成签到,获得积分10
3秒前
了晨完成签到 ,获得积分10
4秒前
yi完成签到 ,获得积分10
7秒前
wxnice完成签到,获得积分10
8秒前
量子星尘发布了新的文献求助10
10秒前
星辰大海应助大橙子采纳,获得10
19秒前
19秒前
七QI完成签到 ,获得积分10
20秒前
23秒前
褚香旋完成签到,获得积分10
23秒前
一只狗东西完成签到 ,获得积分10
25秒前
宇老师发布了新的文献求助10
26秒前
27秒前
qiqi发布了新的文献求助30
29秒前
大橙子发布了新的文献求助10
32秒前
wzhang完成签到,获得积分10
33秒前
ken131完成签到 ,获得积分10
36秒前
myl完成签到,获得积分10
37秒前
728完成签到,获得积分10
43秒前
xiaofeng5838完成签到,获得积分10
43秒前
ronnie完成签到,获得积分10
43秒前
46秒前
寒冷芷蕊完成签到,获得积分20
46秒前
46秒前
Jane完成签到,获得积分10
46秒前
一氧化二氢完成签到,获得积分10
52秒前
凡事发生必有利于我完成签到,获得积分10
53秒前
yihaiqin完成签到 ,获得积分10
57秒前
轩辕剑身完成签到,获得积分0
57秒前
coolkid完成签到 ,获得积分0
58秒前
你怎么那么美完成签到,获得积分10
58秒前
游艺完成签到 ,获得积分10
1分钟前
冬月完成签到 ,获得积分10
1分钟前
薛乎虚完成签到 ,获得积分10
1分钟前
1分钟前
大胖完成签到,获得积分10
1分钟前
野火197完成签到,获得积分10
1分钟前
1分钟前
量子星尘发布了新的文献求助10
1分钟前
April完成签到,获得积分10
1分钟前
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
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038157
求助须知:如何正确求助?哪些是违规求助? 3575869
关于积分的说明 11373842
捐赠科研通 3305650
什么是DOI,文献DOI怎么找? 1819255
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022