Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model

Heming Zhang, Jiarui Feng, Amanda Zeng, Philip Payne, Fuhai Li

Research output: Contribution to journalArticlepeer-review

Abstract

Drug combinations targeting multiple targets/pathways are believed to be able to reduce drug resistance. Computational models are essential for novel drug combination discovery. In this study, we proposed a new simplified deep learning model, DeepSignalingSynergy, for drug combination prediction. Compared with existing models that use a large number of chemical-structure and genomics features in densely connected layers, we built the model on a small set of cancer signaling pathways, which can mimic the integration of multi-omics data and drug target/mechanism in a more biological meaningful and explainable manner. The evaluation results of the model using the NCI ALMANAC drug combination screening data indicated the feasibility of drug combination prediction using a small set of signaling pathways. Interestingly, the model analysis suggested the importance of heterogeneity of the 46 signaling pathways, which indicates that some new signaling pathways should be targeted to discover novel synergistic drug combinations.

Original languageEnglish (US)
Pages (from-to)1364-1372
Number of pages9
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2020
StatePublished - 2020

ASJC Scopus subject areas

  • Medicine(all)

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