DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases

Combination therapy is an important direction of continuous
exploration
in the field of medicine, with the core goals of improving treatment
efficacy, reducing adverse reactions, and optimizing clinical outcomes.
Machine learning technology holds great promise in improving the prediction
of drug synergy combinations. However, most studies focus on single
disease-oriented collaborative predictive models or involve excessive
feature categories, making it challenging to predict the majority
of new drugs. To address these challenges, the DrugSK comprehensive
model was developed, which utilizes SMILES-BERT to extract structural
information from 3492 drugs and trains on reactions from 48,756 drug
combinations. DrugSK is an integrated learning model capable of predicting
interactions among various drug categories. First, the primary learner
is trained from the initial data set. Random forest, support vector
machine, and XGboost model are selected as primary learners and logistic
regression as secondary learners. A new data set is then “generated”
to train level 2 learners, which can be thought of as a prediction
for each model. Finally, the results are filtered using logistic regression.
Furthermore, the combination of the new antibacterial drug Drafloxacin
with other antibacterial agents was tested. The synergistic effect
of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment
for the clinical treatment of skin infection. DrugSK’s prediction
is accurate in practical application and can also predict the probability
of the outcome. In addition, the tendency of Drafloxacin and antifungal
drugs to be synergistic was found. The development of DrugSK will
provide a new blueprint for predicting drug combination synergies.