Cell Morphology-Guided Small Molecule Generation with GFlowNets

Stephen Zhewen Lu, Ziqing Lu, Ehsan Hajiramezanali, Tommaso Biancalani, Yoshua Bengio, Gabriele Scalia, Michał Koziarski

Keywords: drug discovery, generative models, GFlowNets, contrastive learning, multimodal data

TL;DR: Molecular generation using GFlowNets guided by the latent representation from a cross-modal contrastive model

Abstract: High-content phenotypic screening, including high-content imaging (HCI), has gained popularity in the last few years for its ability to characterize novel therapeutics without prior knowledge of the protein target. This work focuses on the novel task of HCI-guided molecular design. We consider an approach in which we leverage an unsupervised multimodal joint embedding to define a latent similarity as a reward for GFlowNets. The proposed model learns to generate new molecules that could produce phenotypic effects similar to those of the given image target, without relying on pre-annotated phenotypic labels. We demonstrate that our method generates molecules with high morphological and structural similarity to the target, increasing the likelihood of similar biological activity.

Submission Number: 101