108 citations · 109 across the 6 of their papers we have counts for
7 papers
CellChain: Benchmarking Cross-Scale Evidence Use for Perturbation Reasoning
Xue Xia, Yue Wang, Shuizhou Chen +6
Recent advances in foundation models and large-scale cellular perturbation datasets have driven growing interest in using LLMs to predict and reason about cellular responses. Biolo…
SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery
SciForge Team, Zhangyang Gao, Minghao Fang +10
Scientific work increasingly spans heterogeneous artifacts -- papers, code, datasets, scientific file formats, model outputs, figures, manuscripts, and team decisions -- yet genera…
What drives performance in molecular MPNNs? An operator-level factorial benchmark
Panyu Jiao, Shuizhou Chen, Yiheng Shen +3
Message-passing neural networks (MPNNs) are widely used for molecular property prediction, but their deployment as monolithic architectures makes it difficult to identify how speci…
Benchmarking virtual cell models for in-the-wild perturbation response
Xinjie Mao, Songming Zhang, Qianhong Wen +10
Virtual cell (VC) models aim to predict cellular responses to any perturbations in silico and have emerged as a promising approach for drug discovery and precision medicine. Yet, a…
SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction
Shuizhou Chen, Lang Yu, Xueqin Lin +12
Virtual-cell models aim to predict how cell populations respond to perturbations, but control and treated cells are measured as unpaired populations, complicating the learning of p…
Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning
Jielan Li, Zekun Chen, Qian Wang +21
Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains…
MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
Han Yang, Chenxi Hu, Yichi Zhou +19
Accurate and fast prediction of materials properties is central to the digital transformation of materials design. However, the vast design space and diverse operating conditions p…