Cross-Modal Graph Contrastive Learning with Cellular Images.

ABSTRACT: Constructing discriminative representations of molecules lies at the core of a number of domains such as drug discovery, chemistry, and medicine. State-of-the-art methods employ graph neural networks and self-supervised learning (SSL) to learn unlabeled data for structural representations, which can then be fine-tuned for downstream tasks. Albeit powerful, these methods are pre-trained solely on molecular structures and thus often struggle with tasks involved in intricate biological processes. Here, it is proposed to assist the learning of molecular representation by using the perturbed high-content cell microscopy images at the phenotypic level. To incorporate the cross-modal pre-training, a unified framework is constructed to align them through multiple types of contrastive loss function

SUBMITTER: Zheng S 

PROVIDER: S-EPMC11348220 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature