# Drug cell line interaction prediction

## TL;DR

This paper introduces tCNNS, a convolutional neural network model that predicts drug responses in cancer cell lines using drug structures in SMILES format, outperforming previous models.

## Contribution

The paper presents a novel CNN-based model, tCNNS, that directly uses drug structures in SMILES format for phenotypic screening, achieving superior predictive performance.

## Key findings

- Achieved R^2 of 0.84 and Pearson correlation of 0.92.
- Outperformed previous models in drug response prediction.
- Provided insights into phenotypic screening processes.

## Abstract

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.