28 papers
Auditing medical multi-agent AI reveals risks of false consensus
Yinghao Zhu, Lei Gu, Zixiang Wang +11
Large language models are increasingly being assembled into medical multi-agent systems that emulate multidisciplinary consultation through specialist roles, peer review and consen…
Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine
Peisong Zhang, Manqiang Peng, Yuxuan Wu +21
Estimating individualized treatment effects from longitudinal observational data is central to data-driven medicine, yet existing methods face a fundamental limitation: reducing co…
Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
Dannong Wang, Jintai Chen, Yingzhou Lu +5
Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designing novel molecular structures to…
PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking
Yize Jiang, Xinze Li, Yuanyuan Zhang +13
Existing protein-ligand docking studies typically focus on the self-docking scenario, which is less practical in real applications. Moreover, some studies involve heavy frameworks…
ClinicalReTrial: Clinical Trial Redesign with Self-Evolving Agents
Sixue Xing, Kerui Wu, Xuanye Xia +3
Clinical trials constitute a critical yet exceptionally challenging and costly stage of drug development ($2.6B per drug), where protocols are encoded as complex natural language…
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…