Version 1
: Received: 10 June 2024 / Approved: 11 June 2024 / Online: 12 June 2024 (00:02:26 CEST)
How to cite:
Abdollahi Shamami, M.; Asghari Ilani, M.; Teimourpour, B. An Optimized Deep Neural Network Framework for Classification of Drug–Drug Interactions. Preprints2024, 2024060750. https://doi.org/10.20944/preprints202406.0750.v1
Abdollahi Shamami, M.; Asghari Ilani, M.; Teimourpour, B. An Optimized Deep Neural Network Framework for Classification of Drug–Drug Interactions. Preprints 2024, 2024060750. https://doi.org/10.20944/preprints202406.0750.v1
Abdollahi Shamami, M.; Asghari Ilani, M.; Teimourpour, B. An Optimized Deep Neural Network Framework for Classification of Drug–Drug Interactions. Preprints2024, 2024060750. https://doi.org/10.20944/preprints202406.0750.v1
APA Style
Abdollahi Shamami, M., Asghari Ilani, M., & Teimourpour, B. (2024). An Optimized Deep Neural Network Framework for Classification of Drug–Drug Interactions. Preprints. https://doi.org/10.20944/preprints202406.0750.v1
Chicago/Turabian Style
Abdollahi Shamami, M., Mohsen Asghari Ilani and Babak Teimourpour. 2024 "An Optimized Deep Neural Network Framework for Classification of Drug–Drug Interactions" Preprints. https://doi.org/10.20944/preprints202406.0750.v1
Abstract
Accurately predicting drug-drug interactions (DDIs) is crucial for preventing adverse drug events in clinical settings. However, existing methodologies often rely on complex models built from diverse data sources, posing challenges in computational drug discovery. To address the need for precise computational methods in predicting unknown DDIs, this study introduces a novel Deep Neural Network (DNN)-based approach. By leveraging a wide range of drug-related information, including substructure, targets, side effects, pathways, and indications, our method calculates multiple drug similarities. These similarities are then synthesized using a nonlinear fusion method to extract high-level features. Subsequently, a tailored neural network is deployed for interaction prediction. Comparative evaluation against three prominent machine learning classifiers—Extreme Gradient Boosting (XGBoost), Adaptive Gradient Boosting (AdaBoost), and Light Gradient-Boosting Machine (LGBM)—using three benchmark datasets demonstrates the superior performance of DNN. It achieves outstanding accuracy, precision, recall, and F1-score metrics, all reaching 94.6% in cross-validation. Additionally, case studies involving numerous drug pairs confirm the reliability of DNN in accurately predicting unknown DDIs. These findings underscore DNN as a potent and reliable method for DDI prediction, with promising implications for drug discovery and healthcare applications.
Keywords
Drug–Drug Interactions (DDI); Deep Neural Network; XGBoost; AdaBoost; LGBM
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
