MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures

arXiv:2311.16666v2 Announce Type: replace
Abstract: The quest for accurate prediction of drug molecule properties poses a fundamental challenge in the realm of Artificial Intelligence Drug Discovery (AIDD). An effective representation of drug molecules emerges as a pivotal component in this pursuit. Contemporary leading-edge research predominantly resorts to self-supervised learning (SSL) techniques to extract meaningful structural representations from large-scale, unlabeled molecular data, subsequently fine-tuning these representations for an array of downstream tasks. However, an inherent shortcoming of these studies lies in …

abstract artificial artificial intelligence arxiv challenge cs.ai cs.lg discovery drug discovery edge framework fundamental graph image intelligence molecules multimodal physics.chem-ph pivotal prediction q-bio.bm quest realm representation research type