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.
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