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

PLoS Comput Biol
Authors
Keywords
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) using an approach called latent space arithmetic (LSA). To test the generalizability of the strategy, we also trained these VAEs using gene expression data of the same compound perturbations and found that gene expression provides complementary information. We found that the β-VAE and MMD-VAE disentangle morphology signals and reveal a more interpretable latent space. We reliably simulated morphology and gene expression readouts from certain compounds thereby predicting cell states perturbed with compounds of known polypharmacology. Inferring cell state for specific drug mechanisms could aid researchers in developing and identifying targeted therapeutics and categorizing off-target effects in the future.

Year of Publication
2022
Journal
PLoS Comput Biol
Volume
18
Issue
2
Pages
e1009888
Date Published
2022 02
ISSN
1553-7358
DOI
10.1371/journal.pcbi.1009888
PubMed ID
35213530
PubMed Central ID
PMC8906577
Links
Grant list
R35 GM122547 / GM / NIGMS NIH HHS / United States