Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.

ABSTRACT:

Background

Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures.

Results

In this paper, a model called twin Convolutional Neural Network for drugs in SMILES format (tCNNS) is introduced for phenotypic screening. tCNNS uses a convolutional network to extract features for drugs from their simplified molecular input line entry specification (SMILES) format and uses another convolutional network to extract featur

SUBMITTER: Liu P 

PROVIDER: S-EPMC6664725 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature