Cell Morphology-Guided De Novo Hit Design by Conditioning Generative Adversarial Networks on Phenotypic Image Features

Abstract

Developing new small molecules that are bioactive is time-consuming, costly and rarely successful. As a mitigation strategy, we apply, for the first time, generative adversarial networks to de novo design of small molecules using a phenotype-based drug discovery approach. We trained our model on a set of 30,000 compounds and their respective morphological profiles extracted from high content images; no target information was used to train the model. Using this approach, we were able to automatically design agonist-like compounds of different molecular targets.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

“…In addition to the practical implication — that many expensive screens might be replaced effectively with a computational prediction step — this result also indicates that a substantial proportion of biological pathways of interest are captured in a single imaging assay. Furthermore, by combining image profiles with compound structural information, machine learning was able to predict active chemical structures de novo 26 .…”

“…The study illustrated how machine learning can leverage side information — in this case, a large volume of assay activity labels . In another study, machine learning showed potential for image-based lead optimization 26 . Both of these studies were led by researchers at large pharmaceutical companies and reveal a renewed interest in profiling-inspired drug discovery.…”