Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic.

ABSTRACT: A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space. These representations have been generated from various biomedical data types and can be used to produce realistic-looking simulated data. However, standard vanilla VAEs suffer from entangled and uninformative latent spaces, which can be mitigated using other types of VAEs such as β-VAE and MMD-VAE. In this project, we evaluated the ability of VAEs to learn cell morphology characteristics derived from cell images. We trained and evaluated these three VAE variants-Vanilla VAE, β-VAE, and MMD-VAE-on cell morphology readouts and explored the generative capacity of each model to predict compound polypharmacology (the interactions of a drug with more than one target

SUBMITTER: Chow YL 

PROVIDER: S-EPMC8906577 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature