Development and Validation of a Machine Learning-Based Prediction Model for Drug and Food Constituent Interactions from Chemical Structures

Possible drug-food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions lead to the rise of drug-drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament’s effect, the withdrawals of various medications, harmful impacts on the patients’ health. However, the importance of DFIs is remained underestimated, as the number of studies referring to these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, data input, and detailed annotations. This study proposed a novel prediction model to address the limitations from previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. Subsequently, PyBioMed package was used to extract 3,780 features from each drug-food compound pair. Four machine learning (ML) algorithms were assessed, and the optimal model was Random Forest Classifier (RF), which is an ensemble learning model. We also validated the performance of our model on one internal and one external validation set from a previous study which contained 1,562 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions and to evaluate whether it can perform well on Covid-19 oral drugs. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death (e.g., Diazepam and Ethanol). Our proposed model can contribute to the development of predictive models to help doctors and patients avoid the adverse effects of DFIs in clinical practice.