no code implementations • 7 Feb 2024 • Jiahua Rao, Jiancong Xie, Hanjing Lin, Shuangjia Zheng, Zhen Wang, Yuedong Yang
While such methods could improve GNN predictions, they usually don't perform well on explanations.
no code implementations • 6 Feb 2024 • Chenqing Hua, Connor Coley, Guy Wolf, Doina Precup, Shuangjia Zheng
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense.
1 code implementation • 29 Aug 2023 • Jingbang Chen, Yian Wang, Xingwei Qu, Shuangjia Zheng, Yaodong Yang, Hao Dong, Jie Fu
Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules.
1 code implementation • 12 May 2022 • Jiahua Rao, Shuangjia Zheng, Sijie Mai, Yuedong Yang
To address these problems, we propose a novel Communicative Subgraph representation learning for Multi-relational Inductive drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene relations are made through subgraph patterns, and thus are naturally inductive for unseen drugs/genes without retraining or utilizing external domain features.
no code implementations • 30 Nov 2021 • Shuangjia Zheng, Ying Song, Zhang Pan, Chengtao Li, Le Song, Yuedong Yang
Optimizing chemical molecules for desired properties lies at the core of drug development.
no code implementations • 4 Sep 2021 • Sijie Mai, Ying Zeng, Shuangjia Zheng, Haifeng Hu
Specifically, we simultaneously perform intra-/inter-modal contrastive learning and semi-contrastive learning (that is why we call it hybrid contrastive learning), with which the model can fully explore cross-modal interactions, preserve inter-class relationships and reduce the modality gap.
no code implementations • 26 Jul 2021 • Shuangjia Zheng, Sijie Mai, Ya Sun, Haifeng Hu, Yuedong Yang
In this way, we find the model can quickly adapt to few-shot relationships using only a handful of known facts with inductive settings.
1 code implementation • 19 Jul 2021 • Jianwen Chen, Shuangjia Zheng, Ying Song, Jiahua Rao, Yuedong Yang
For this sake, we propose a Communicative Message Passing Transformer (CoMPT) neural network to improve the molecular graph representation by reinforcing message interactions between nodes and edges based on the Transformer architecture.
2 code implementations • 1 Jul 2021 • Jiahua Rao, Shuangjia Zheng, Yuedong Yang
Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and molecular property prediction.
no code implementations • 26 May 2021 • Shuangjia Zheng, Tao Zeng, Chengtao Li, Binghong Chen, Connor W. Coley, Yuedong Yang, Ruibo Wu
Nature, a synthetic master, creates more than 300, 000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs.
1 code implementation • 16 Dec 2020 • Sijie Mai, Shuangjia Zheng, Yuedong Yang, Haifeng Hu
Relation prediction for knowledge graphs aims at predicting missing relationships between entities.
1 code implementation • NeurIPS 2020 • Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, Jinyu Yang, Yang Yu, Junzhou Huang
Retrosynthesis is the process of recursively decomposing target molecules into available building blocks.
1 code implementation • 2 Jul 2019 • Shuangjia Zheng, Jiahua Rao, Zhongyue Zhang, Jun Xu, Yuedong Yang
Synthesis planning is the process of recursively decomposing target molecules into available precursors.