Drug cell line interaction prediction
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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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Dec 2018.
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Citing papers per year: 2022: 3; 2023: 2; 2024: 1; 2025: 1; 2026: 1.
8 papers.
- 2022Deep learning methods for drug response prediction in cancer: predominant and emerging trendscited by 6
- 2022Towards a Better Model with Dual Transformer for Drug Response Predictioncited by 2
- 2023Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screeningcited by 1
- 2023CLDR: Contrastive Learning Drug Response Models from Natural Language Supervisioncited by 1
- 2024Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Datacited by 1
- 2025Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysiscited by 1
- 2022TCR: A Transformer Based Deep Network for Predicting Cancer Drugs Responsecited by 0
- 2026ScreenShot: A Foundation Model for Few-Shot Combination Drug Screeningcited by 0
- 2026ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening12 Aug 2026
- 2025Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis18 Mar 2025
- 2024Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data18 Sep 2024
- 2023CLDR: Contrastive Learning Drug Response Models from Natural Language Supervision17 Dec 2023
- 2023Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening5 Oct 2023
- 2022Deep learning methods for drug response prediction in cancer: predominant and emerging trends18 Nov 2022
- 2022Towards a Better Model with Dual Transformer for Drug Response Prediction23 Oct 2022
- 2022TCR: A Transformer Based Deep Network for Predicting Cancer Drugs Response10 Jul 2022