# Variational Autoencoder for Anti-Cancer Drug Response Prediction

arXiv: https://arxiv.org/abs/2008.09763 · HTML: https://mlsanity.com/paper/2008.09763/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2020.

## Built on by (most cited first, 5 of 5)

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- [When Large Language Models Meet Vector Databases: A Survey](https://mlsanity.com/paper/2402.01763/) (2024)
- [Deep learning methods for drug response prediction in cancer: predominant and emerging trends](https://mlsanity.com/paper/2211.10442/) (2022)
- [Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning](https://mlsanity.com/paper/2403.06108/) (2024)
- [A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction](https://mlsanity.com/paper/2206.03555/) (2022)

## Abstract (arXiv)

Cancer is a primary cause of human death, but discovering drugs and tailoring cancer therapies are expensive and time-consuming. We seek to facilitate the discovery of new drugs and treatment strategies for cancer using variational autoencoders (VAEs) and multi-layer perceptrons (MLPs) to predict anti-cancer drug responses. Our model takes as input gene expression data of cancer cell lines and anti-cancer drug molecular data and encodes these data with our sc GeneVae model, which is an ordinary VAE model, and a rectified junction tree variational autoencoder (sc JTVae) model, respectively. A multi-layer perceptron processes these encoded features to produce a final prediction. Our tests show our system attains a high average coefficient of determination (R2 = 0.83) in predicting drug responses for breast cancer cell lines and an average R2 = 0.845 for pan-cancer cell lines. Additionally, we show that our model can generates effective drug compounds not previously used for specific cancer cell lines.