14 citations · 17 across the 5 of their papers we have counts for
5 papers
Phi-Former: A Pairwise Hierarchical Approach for Compound-Protein Interactions Prediction
Zhe Wang, Zijing Liu, Chencheng Xu +1
Drug discovery remains time-consuming, labor-intensive, and expensive, often requiring years and substantial investment per drug candidate. Predicting compound-protein interactions…
Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models
Weidi Luo, He Cao, Zijing Liu +5
With the extensive deployment of Large Language Models (LLMs), ensuring their safety has become increasingly critical. However, existing defense methods often struggle with two key…
PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes
He Cao, Yanjun Shao, Zhiyuan Liu +4
Multimodal Large Language Models (MLLMs) have seen growing adoption across various scientific disciplines. These advancements encourage the investigation of molecule-text modeling…
MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension
Xingyu Lu, He Cao, Zijing Liu +5
Large language models are playing an increasingly significant role in molecular research, yet existing models often generate erroneous information, posing challenges to accurate mo…
InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery
He Cao, Zijing Liu, Xingyu Lu +2
The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in…