Prediction of Drug Synergy by Ensemble Learning

TL;DR

This paper explores the use of ensemble learning with novel compound representations to improve the prediction of drug synergy in combinational cancer therapy, demonstrating superior performance over baseline models.

Contribution

It introduces a new compound representation for drug synergy prediction and proposes an ensemble approach that combines multiple representation-model pairs for enhanced accuracy.

Findings

Ensemble models outperform individual baseline models.

A novel compound representation improves prediction accuracy.

The approach is effective on large drug combination datasets.

Abstract

One of the promising methods for the treatment of complex diseases such as cancer is combinational therapy. Due to the combinatorial complexity, machine learning models can be useful in this field, where significant improvements have recently been achieved in determination of synergistic combinations. In this study, we investigate the effectiveness of different compound representations in predicting the drug synergy. On a large drug combination screen dataset, we first demonstrate the use of a promising representation that has not been used for this problem before, then we propose an ensemble on representation-model combinations that outperform each of the baseline models.