A Machine Learning Method for Drug Combination Prediction

Frontiers in Genetics
Jiang LiHong-Yu Zhang

Abstract

Drug combination is now a hot research topic in the pharmaceutical industry, but experiment-based methodologies are extremely costly in time and money. Many computational methods have been proposed to address these problems by starting from existing drug combinations. However, in most cases, only molecular structure information is included, which covers too limited a set of drug characteristics to efficiently screen drug combinations. Here, we integrated similarity-based multifeature drug data to improve the prediction accuracy by using the neighbor recommender method combined with ensemble learning algorithms. By conducting feature assessment analysis, we selected the most useful drug features and achieved 0.964 AUC in the ensemble models. The comparison results showed that the ensemble models outperform traditional machine learning algorithms such as support vector machine (SVM), naïve Bayes (NB), and logistic regression (GLM). Furthermore, we predicted 7 candidate drug combinations for a specific drug, paclitaxel, and successfully verified that the two of the predicted combinations have promising effects.

References

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Citations

Jun 28, 2021·European Journal of Medicinal Chemistry·Noura KhaiwaTaleb H Al-Tel
Sep 3, 2021·Journal of the American Medical Informatics Association : JAMIA·Jiannan YangQingpeng Zhang
Sep 4, 2021·Briefings in Bioinformatics·Lianlian WuXiaochen Bo
Nov 17, 2021·IET Systems Biology·Wei WangXianfang Wang

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Methods Mentioned

BETA
Assay

Software Mentioned

CMap
CompuSyn
UMLS
R
GraphPad Prism
R package
PubChem
randomForest

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