  Understanding the phenotypic drug response on cancer cell lines plays a vital
rule 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 test their models and methods. Previously, most
research in these areas starts from the fingerprints or features of drugs,
instead of their structures. In this paper, we introduce a model for phenotypic
screening, which is called twin Convolutional Neural Network for drugs in
SMILES format (tCNNS). tCNNS is comprised of CNN input channels for drugs in
SMILES format and cancer cell lines respectively. Our model achieves $0.84$ for
the coefficient of determinant($R^2$) and $0.92$ for Pearson
correlation($R_p$), which are significantly better than previous
works\cite{ammad2014integrative,haider2015copula,menden2013machine}. Besides
these statistical metrics, tCNNS also provides some insights into phenotypic
screening.
