# Deep learning methods for drug response prediction in cancer: predominant and emerging trends

arXiv: https://arxiv.org/abs/2211.10442 · HTML: https://mlsanity.com/paper/2211.10442/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 175 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 30 of 30)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [DataPerf: Benchmarks for Data-Centric AI Development](https://mlsanity.com/paper/2207.10062/) (2022)
- [Graph Neural Networks in TensorFlow and Keras with Spektral](https://mlsanity.com/paper/2006.12138/) (2020)
- [SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules](https://mlsanity.com/paper/1703.07076/) (2017)
- [DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science](https://mlsanity.com/paper/2106.14232/) (2021)
- [Removal of Batch Effects using Distribution-Matching Residual Networks](https://mlsanity.com/paper/1610.04181/) (2016)
- [Drug cell line interaction prediction](https://mlsanity.com/paper/1812.11178/) (2018)
- [Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders](https://mlsanity.com/paper/1904.11223/) (2019)
- [Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response](https://mlsanity.com/paper/2005.09572/) (2020)
- [Variational Autoencoder for Anti-Cancer Drug Response Prediction](https://mlsanity.com/paper/2008.09763/) (2020)
- [Predicting drug response of tumors from integrated genomic profiles by deep neural networks](https://mlsanity.com/paper/1805.07702/) (2018)
- [Learning Curves for Drug Response Prediction in Cancer Cell Lines](https://mlsanity.com/paper/2011.12466/) (2020)
- [AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks](https://mlsanity.com/paper/2112.08366/) (2021)
- [PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks](https://mlsanity.com/paper/1811.06802/) (2018)
- [A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning](https://mlsanity.com/paper/2005.00095/) (2020)
- [Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks](https://mlsanity.com/paper/2211.02720/) (2022)
- [Deep Denerative Models for Drug Design and Response](https://mlsanity.com/paper/2109.06469/) (2021)

## Built on by (most cited first, 6 of 6)

- [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)
- [Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses](https://mlsanity.com/paper/2407.04486/) (2024)
- [Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain Shift](https://mlsanity.com/paper/2603.16185/) (2026)
- [Large-scale AI-Ready Data for Anti-Cancer Drug Response Modeling](https://mlsanity.com/paper/2608.11444/) (2026)
- [PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction](https://mlsanity.com/paper/2608.21349/) (2026)

## Abstract (arXiv)

Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 60 deep learning-based models have been curated and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.