Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features

Abstract

The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for histamine antagonist drugs using simplified molecular-input line-entry systems (SMILES) combined with interaction features based on CYP450 group as inputs. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 26,344 DDI pairs from the DrugBank database. Various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost were used with 5-fold cross-validation to approach a large-scale DDIs prediction among histamine antagonist drugs. The prediction performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.788, a recall of 0.921, and an F1-score of 0.838 among 19 given DDIs types. An important finding of the study is that our prediction is based solely on the SMILES and CYP450 and thus can be applied at the early stage of drug development.

On the road to explainable AI in drug-drug interactions prediction: A systematic review

“…Furthermore, gradient boosting-based algorithm XGBoost was employed to achieve robust DDI prediction even for drugs whose interaction profiles were completely unseen during training [60] . XGBoost performed better or comparable to other algorithms, such as SVM, random forest, and the standard gradient boosting in terms of predictive performance and speed in DDIs prediction [49] , [60] .…”

“…These SMILES structural representations of drugs are post-processed to capture features of drug pairs associated with DDIs events [45] . Moreover, pharmacological properties such as targets [8] , [46] , enzymes, transporters, genes and proteins [6] , [47] , interaction pathways like enzymes and transporters [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] can also be manipulated to represent drugs features through a set of descriptors. Network interaction mining [62] , [63] , [64] and molecular graph representations have also been used to describe substructures of drugs that come in distinctive shapes and sizes or the structural relations between entities [65] , [66] , [67] , [68] .…”

Prediction of drug-drug interaction events using graph neural networks based feature extraction

“…Cheng et al 10 combined a variety of drug-drug similarities to represent drug-drug pairs and utilized five classifiers to construct the prediction models. Dang et al 11 adopt a machine learning model to predict DDI types for histamine antagonist drugs using two similarity matrices as inputs. Then employ various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost for DDIs prediction.…”

GNN-DDI: A New Data Integration Framework for Predicting Drug-Drug Interaction Events Based on Graph Neural Networks

“…Cheng et al 16 combined a variety of drugdrug similarities to represent drug-drug pairs and utilised ve classi ers to construct the prediction models. Dang et al 17 Adopt a machine learning model to predict DDI types for histamine antagonist drugs using two similarity matrices as inputs. Then employ various classi cation algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost for DDIs prediction.…”