17 citations · 27 across the 6 of their papers we have counts for
6 papers
Delete: Deep Lead Optimization Enveloped in Protein Pocket through Unified Deleting Strategies and a Structure-aware Network
Haotian Zhang, Huifeng Zhao, Xujun Zhang +10
Drug discovery is a highly complicated process, and it is unfeasible to fully commit it to the recently developed molecular generation methods. Deep learning-based lead optimizatio…
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop
Tianyue Wang, Xujun Zhang, Odin Zhang +5
Protein loop modeling is the most challenging yet highly non-trivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab i…
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
Zhongwei Wan, Yichun Yin, Wei Zhang +5
Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs…
Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction
Chen Liang, Bowen Wang, Shaogang Hao +3
Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a high capacity to establish connections between the structure and propert…
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
Zhongwei Wan, Xin Liu, Benyou Wang +5
Session-based recommendation (SBR) systems aim to utilize the user's short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try…
Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
Pengfei Chen, Weiwen Liu, Chang-Yu Hsieh +2
Graph Neural Networks (GNNs) achieve an impressive performance on structured graphs by recursively updating the representation vector of each node based on its neighbors, during wh…