# Drug cell line interaction prediction

arXiv: https://arxiv.org/abs/1812.11178 · HTML: https://mlsanity.com/paper/1812.11178/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2018.

## Built on by (most cited first, 8 of 8)

- [Deep learning methods for drug response prediction in cancer: predominant and emerging trends](https://mlsanity.com/paper/2211.10442/) (2022)
- [Towards a Better Model with Dual Transformer for Drug Response Prediction](https://mlsanity.com/paper/2210.17401/) (2022)
- [Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening](https://mlsanity.com/paper/2310.12996/) (2023)
- [CLDR: Contrastive Learning Drug Response Models from Natural Language Supervision](https://mlsanity.com/paper/2312.10707/) (2023)
- [Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data](https://mlsanity.com/paper/2409.12215/) (2024)
- [Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis](https://mlsanity.com/paper/2503.14356/) (2025)
- [TCR: A Transformer Based Deep Network for Predicting Cancer Drugs Response](https://mlsanity.com/paper/2207.04457/) (2022)
- [ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening](https://mlsanity.com/paper/2608.12219/) (2026)

## Abstract (arXiv)

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(R2) and 0.92 for Pearson correlation(Rp), which are significantly better than previous worksciteammad2014integrative,haider2015copula,menden2013machine. Besides these statistical metrics, tCNNS also provides some insights into phenotypic screening.