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.

中文翻译：

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使用药物卷积神经网络预测药物不良反应

药物不良反应 (ADR) 的预测因其对制药行业的影响而成为药物警戒的一个重要方面。将新药引入市场的标准过程涉及大量的临床试验和测试。这是一个乏味且耗时的过程，并且还涉及大量金钱资源。药物的更快批准有助于需要该药物的患者。药物不良反应的计算机预测可以帮助加快上述过程。所涉及的挑战是缺乏负面数据存在和仅从化学结构预测 ADR。尽管已经有许多模型可用于预测 ADR，但大多数模型除了药物的化学结构外，还使用生物活性标识符、化学和物理特性。但是对于大多数要测试的新药来说，只有化学结构可用。仅使用化学结构预测 ADR 的现有模型的性能不高。因此，本文提出了一种仅从化学结构来有效预测 ADR 的方法。所提出的方法涉及每个 ADR 的单独模型，使其成为二元分类问题。本文提出了一种称为药物卷积神经网络 (DCNN) 的新型 CNN 模型，用于使用药物的化学结构预测 ADR。使用准确度、召回率、精确度、特异性、F1 分数、AUROC 和 MCC 等指标来衡量性能。就所有指标而言，所提出的 DCNN 模型获得的结果优于 SIDER4.1 数据库上的竞争模型。