9 citations · 39 across the 14 of their papers we have counts for
8 papers · 1 filter
Protein-Mamba: Biological Mamba Models for Protein Function Prediction
Bohao Xu, Yingzhou Lu, Yoshitaka Inoue +3
Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics. Traditional machine learning models oft…
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction
Bohao Xu, Yingzhou Lu, Chenhao Li +5
In drug discovery, predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small-molecule drugs is critical for ensuring safety and effic…
BioMamba: Domain-Adaptive Biomedical Language Models
Ling Yue, Mingzhi Zhu, Sixue Xing +8
Background. Biomedical language models should improve performance on biomedical text while retaining general-language-modeling fluency. For Mamba-based models, this trade-off has n…
DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing
Yingzhou Lu, Yaojun Hu, Chenhao Li
Bringing a novel drug from the original idea to market typically requires more than ten years and billions of dollars. To alleviate the heavy burden, a natural idea is to reuse the…
TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets
Jintai Chen, Yaojun Hu, Mingchen Cai +13
Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning ove…
Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?
Kangyu Zheng, Yingzhou Lu, Zaixi Zhang +4
Currently, the field of structure-based drug design is dominated by three main types of algorithms: search-based algorithms, deep generative models, and reinforcement learning. Whi…
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
Yue Wang, Tianfan Fu, Yinlong Xu +6
Clinical trials are indispensable for medical research and the development of new treatments. However, clinical trials often involve thousands of participants and can span several…
Uncertainty Quantification on Clinical Trial Outcome Prediction
Tianyi Chen, Yingzhou Lu, Nan Hao +4
The importance of uncertainty quantification is increasingly recognized in the diverse field of machine learning. Accurately assessing model prediction uncertainty can help provide…