1 code implementation • 16 Oct 2023 • Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen
Particularly in biomedical relationship prediction tasks, NC-KGE outperforms all baselines on datasets such as PharmKG8k-28, DRKG17k-21, and BioKG72k-14, especially in predicting drug combination relationships.
1 code implementation • 1 Aug 2023 • Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen
In this paper, an equivariant consistency model (EC-Conf) was proposed as a fast diffusion method for low-energy conformation generation.
no code implementations • 13 May 2023 • Guihong Li, Kartikeya Bhardwaj, Yuedong Yang, Radu Marculescu
Anytime neural networks (AnytimeNNs) are a promising solution to adaptively adjust the model complexity at runtime under various hardware resource constraints.
no code implementations • 27 Apr 2023 • Jiahua Rao, Zifei Shan, Longpo Liu, Yao Zhou, Yuedong Yang
With the recent progress in large-scale vision and language representation learning, Vision Language Pre-training (VLP) models have achieved promising improvements on various multi-modal downstream tasks.
1 code implementation • 26 Jan 2023 • Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, Radu Marculescu
Based on this theoretical analysis, we propose a new zero-shot proxy, ZiCo, the first proxy that works consistently better than #Params.
1 code implementation • CVPR 2023 • Yuedong Yang, Guihong Li, Radu Marculescu
Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI.
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 • 31 Jan 2022 • Zihui Xue, Yuedong Yang, Mengtian Yang, Radu Marculescu
Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition.
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 • 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 • 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.