7 citations · 8 across the 10 of their papers we have counts for
13 papers
Coverage Before Control: Route-Instruction Grounding and Steering for Controllable Retrosynthesis
Xuemin Chen, Xiaozhuang Song, Xinjian Zhao +2
Single-step retrosynthesis models are commonly evaluated by their ability to recover recorded reactions. In practice, chemists may need to choose among several precursor sets for t…
RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis
Xiaozhuang Song, Xuemin Chen, Xinjian Zhao +2
Retrosynthesis enables the discovery of viable synthetic routes to target molecules. It plays a central role in modern drug discovery and materials design. Retrosynthesis involves…
When Vision Meets Graphs: A Survey on Graph Reasoning and Learning
Xinjian Zhao, Wei Pang, Zhixuan Yu +8
Graphs are a fundamental data structure underlying many problems in the natural and social sciences. Over the past decade, Graph Neural Networks (GNNs) have dominated graph machine…
MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models
Xinjian Zhao, Xiangru Jian, Yaoyao Xu +4
Molecular embedding models can serve as foundational infrastructure for computational chemistry and drug discovery, where reusable vector representations support property predictio…
Closed-Loop Molecular Inverse Design with Semantic LLM Surrogates
Yaoyao Xu, Xinjian Zhao, Xiaozhuang Song +2
Practical molecular inverse design is rarely a one-shot generation problem; it often involves enriching a pool of molecules that match a desired property profile under a limited or…
HT-GNN: Hyper-Temporal Graph Neural Network for Customer Lifetime Value Prediction in Baidu Ads
Xiaohui Zhao, Xinjian Zhao, Jiahui Zhang +3
Lifetime value (LTV) prediction is crucial for news feed advertising, enabling platforms to optimize bidding and budget allocation for long-term revenue growth. However, it faces t…
The Underappreciated Power of Vision Models for Graph Structural Understanding
Xinjian Zhao, Wei Pang, Zhongkai Xue +6
Graph Neural Networks operate through bottom-up message-passing, fundamentally differing from human visual perception, which intuitively captures global structures first. We invest…
AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search
Xiaozhuang Song, Xuanhao Pan, Xinjian Zhao +4
Retrosynthesis planning enables the discovery of viable synthetic routes for target molecules, playing a crucial role in domains like drug discovery and materials design. Multi-ste…
Boosting Protein Language Models with Negative Sample Mining
Yaoyao Xu, Xinjian Zhao, Xiaozhuang Song +2
We introduce a pioneering methodology for boosting large language models in the domain of protein representation learning. Our primary contribution lies in the refinement process f…
Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning
Xiangru Jian, Xinjian Zhao, Wei Pang +4
The recent surge in contrast-based graph self-supervised learning has prominently featured an intensified exploration of spectral cues. Spectral augmentation, which involves modify…
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation
Xinjian Zhao, Liang Zhang, Yang Liu +2
Graph contrastive learning (GCL) has emerged as a pivotal technique in the domain of graph representation learning. A crucial aspect of effective GCL is the caliber of generated po…
Boosting Graph Pooling with Persistent Homology
Chaolong Ying, Xinjian Zhao, Tianshu Yu
Recently, there has been an emerging trend to integrate persistent homology (PH) into graph neural networks (GNNs) to enrich expressive power. However, naively plugging PH features…
Embedding in Recommender Systems: A Survey
Maolin Wang, Xinjian Zhao, Wanyu Wang +9
Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that conv…