Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

Abstract

Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases

“…111 Efficient, high-throughput identification of effective drug combinations is therefore an important component of a successful therapeutic discovery pipeline. To this end, a number of methods for computational prediction of synergistic compounds have been developed, 112 based on side-effect profiles, 108 chemical and pathway data, 113 network analyses, 114 and drug-induced gene expression patterns. 109 Curated databases of reported drug combinations and other resources are also publicly available.…”

comboFM: leveraging multi-way interactions for systematic prediction of drug combination effects

“…To investigate the benefit of considering higher-order feature interactions, we also performed experiments using both second order formulation of FMs and first order FMs (corresponding to ridge regression). To further benchmark the predictive performance of comboFM, we applied random forest (RF) as a reference model, a widely-used machine learning model that is based on a rather different learning principle, and has previously been used for modeling drug combination effects [24,25,26,27,28], including the winning method of the recent AstraZeneca-Sanger drug combination prediction DREAM Challenge [19]. The cross-validation folds were held fixed throughout the experiments to ensure a fair comparison.…”

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

“…To this end, several algorithms have been proposed to predict synergistic drug pairs which utilize a diverse set of features such as chemical structure, biological networks interactions (e.g., drug-protein, protein -disease, etc.) and omics data [6,7,8,9,10,11,12,13,14,15]. While feature engineering and a systems approach come with the promise of performance increase, features other than the chemical structure are not always available, and biological networks such as protein interaction networks are incomplete.…”