Abstract: Molecular pretraining models,as an important direction integrating artificial intelligence with drug discovery,have shown remarkable advantages in tasks such as molecular property prediction,virtual screening,and molecular design.These me-thods learn transferable molecular representations through self-supervised training on large-scale unlabeled molecular data,effectively alleviating the challenges of limited labeled data and insufficient task generalization in drug development.This review systematically summarizes the recent progress of molecular pretraining models in the field of drug discovery,focusing on the development of molecular representations.Mainstream model architectures and pretraining strategies are summarized,and their perfor-mance and advantages in key applications such as property prediction,virtual screening,and molecular generation are analyzed.Finally,the current challenges faced by molecular pretraining models,including data quality,explicit modeling,multimodal fusion,and interpretability are discussed,and perspectives on future directions are provided to serve as a reference for relatedresearch and applications.

Key words: Molecular pretraining models, Drug discovery, Self-supervised learning, Graph neural networks, Transformer