Prediction of adverse drug reactions using drug convolutional neural networks.
| Title: | Prediction of adverse drug reactions using drug convolutional neural networks. |
|---|---|
| Authors: | Mantripragada AS; Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai 600127, India.; Teja SP; Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai 600127, India.; Katasani RR; Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai 600127, India.; Joshi P; Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai 600127, India.; V M; Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai 600127, India.; Ramesh R; Data Foundry, Bangalore, India. |
| Source: | Journal of bioinformatics and computational biology [J Bioinform Comput Biol] 2021 Feb; Vol. 19 (1), pp. 2050046. Date of Electronic Publication: 2021 Jan 20. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Language: | English |
| Journal Info: | Publisher: World Scientific Publishing Europe Ltd Country of Publication: Singapore NLM ID: 101187344 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1757-6334 (Electronic) Linking ISSN: 02197200 NLM ISO Abbreviation: J Bioinform Comput Biol Subsets: MEDLINE |
| Imprint Name(s): | Publication: [Singapore] : World Scientific Publishing Europe Ltd.; Original Publication: London : Imperial College Press, c2003- |
| MeSH Terms: | Drug-Related Side Effects and Adverse Reactions* ; Neural Networks, Computer*; Antiviral Agents/*adverse effects; Adenosine Monophosphate/adverse effects ; Adenosine Monophosphate/analogs & derivatives ; Alanine/adverse effects ; Alanine/analogs & derivatives ; Dexamethasone/adverse effects ; Databases, Pharmaceutical ; Deep Learning ; Humans ; COVID-19 Drug Treatment |
| 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. |
| Contributed Indexing: | Keywords: Adverse drug reactions; CNN; COVID-19; deep learning; health informatics; machine learning; pharmacovigilance |
| Substance Nomenclature: | 0 (Antiviral Agents); 3QKI37EEHE (remdesivir); 415SHH325A (Adenosine Monophosphate); 7S5I7G3JQL (Dexamethasone); OF5P57N2ZX (Alanine) |
| Entry Date(s): | Date Created: 20210121 Date Completed: 20210312 Latest Revision: 20221207 |
| Update Code: | 20260130 |
| DOI: | 10.1142/S0219720020500468 |
| PMID: | 33472571 |
| Database: | MEDLINE |
Journal Article; Research Support, Non-U.S. Gov't