51 citations · 157 across the 14 of their papers we have counts for
16 papers
LLM: Multi-view Molecular Representation Learning with Large Language Models
Jiaxin Ju, Yizhen Zheng, Huan Yee Koh +2
Accurate molecular property prediction is a critical challenge with wide-ranging applications in chemistry, materials science, and drug discovery. Molecular representation methods,…
ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models
Zhuo Chen, Yizhen Zheng, Huan Yee Koh +4
Molecular Relational Learning (MRL) aims to understand interactions between molecular pairs, playing a critical role in advancing biochemical research. With the recent development…
A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection
Junjun Pan, Yixin Liu, Xin Zheng +4
Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic conn…
Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization
Jiajun Yu, Yizhen Zheng, Huan Yee Koh +3
Molecular optimization is a crucial yet complex and time-intensive process that often acts as a bottleneck for drug development. Traditional methods rely heavily on trial and error…
Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials
Yizhen Zheng, Huan Yee Koh, Maddie Yang +5
The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding dis…
PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection
Junjun Pan, Yixin Liu, Yizhen Zheng +1
Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in various domains such as medicine, social networks, and e…