3 citations · 11 across the 7 of their papers we have counts for
8 papers
A 3D pocket-aware and affinity-guided diffusion model for lead optimization
Anjie Qiao, Junjie Xie, Weifeng Huang +7
Molecular optimization, aimed at improving binding affinity or other molecular properties, is a crucial task in drug discovery that often relies on the expertise of medicinal chemi…
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
Jiahua Rao, Jiancong Xie, Hanjing Lin +3
Graph Neural Networks (GNNs) have gained considerable traction for their capability to effectively process topological data, yet their interpretability remains a critical concern.…
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators
Jingbang Chen, Yian Wang, Xingwei Qu +4
Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of parti…
Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction
Jiahua Rao, Shuangjia Zheng, Sijie Mai +1
Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene intera…
Molecular Attributes Transfer from Non-Parallel Data
Shuangjia Zheng, Ying Song, Zhang Pan +3
Optimizing chemical molecules for desired properties lies at the core of drug development. Despite initial successes made by deep generative models and reinforcement learning metho…
Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning
Shuangjia Zheng, Sijie Mai, Ya Sun +2
Link prediction for knowledge graphs aims to predict missing connections between entities. Prevailing methods are limited to a transductive setting and hard to process unseen entit…