1 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
Text-guided multi-property molecular optimization with a diffusion language model
Yida Xiong, Kun Li, Jiameng Chen +4
Molecular optimization (MO) is a crucial stage in drug discovery in which task-oriented generated molecules are optimized to meet practical industrial requirements. Existing mainst…
Dual-perspective Cross Contrastive Learning in Graph Transformers
Zelin Yao, Chuang Liu, Xueqi Ma +5
Graph contrastive learning (GCL) is a popular method for leaning graph representations by maximizing the consistency of features across augmented views. Traditional GCL methods uti…
A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction
Hongzhi Zhang, Xiuwen Gong, Shirui Pan +3
Drug-target interaction (DTI) prediction is a critical component of the drug discovery process. In the drug development engineering field, predicting novel drug-target interactions…
Regressor-free Molecule Generation to Support Drug Response Prediction
Kun Li, Xiuwen Gong, Shirui Pan +3
Drug response prediction (DRP) is a crucial phase in drug discovery, and the most important metric for its evaluation is the IC50 score. DRP results are heavily dependent on the qu…
Hi-GMAE: Hierarchical Graph Masked Autoencoders
Chuang Liu, Zelin Yao, Xueqi Ma +4
Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning approach for graph-structured data. Existing GMAE models primarily focus on reconstructing node…