Prediction of adverse drug reactions using drug convolutional neural networks

Abstract

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.

Could Artificial Intelligence Prevent Intraoperative Anaphylaxis? Reference Review and Proof of Concept

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Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

“…It was found that recent papers can be classified into three types of databases, namely clinical [13], [18], [27], [33], registry [10], [12], [20], [24], [25], [29], [34], and knowledge [14], [15], [16], [17], [19], [21], [22], [23], [26], [28], [30], [31], [32], [35]. Additionally, based on review papers of existing XAI models [48], [49], the XAI algorithms used were divided into the following four categories: surrogate The main advantage of an explanatory technique such as SHAP is that it has solid roots in game theory, which ensures that the explanation of a prediction instance is fairly distributed across the features.…”

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study

“…For example, Kuang et al [7] built machine learning models using topological information from the drug-ADR associations network, drug chemical structures, and drug Anatomical Therapeutic Chemical (ATC) classification information to discover new drug-ADR associations. Anjani and colleagues [8] constructed a convolutional neural network model solely using drug chemical structures for the prediction of ADR occurrence. However, due to the absence of high-dimensional toxicity information such as ADR severity and frequency, current ADR prediction models are insufficient to comprehensively assess the true impact of drug toxicity on human health [9].…”