5 papers
Identifying biological perturbation targets through causal differential networks
Menghua Wu, Umesh Padia, Sean H. Murphy +2
Identifying variables responsible for changes to a biological system enables applications in drug target discovery and cell engineering. Given a pair of observational and intervent…
Thought calibration: Efficient and confident test-time scaling
Menghua Wu, Cai Zhou, Stephen Bates +1
Reasoning large language models achieve impressive test-time scaling by thinking for longer, but this performance gain comes at significant compute cost. Directly limiting test-tim…
Predicting sub-population specific viral evolution
Wenxian Shi, Menghua Wu, Regina Barzilay
Forecasting the change in the distribution of viral variants is crucial for therapeutic design and disease surveillance. This task poses significant modeling challenges due to the…
Sample, estimate, aggregate: A recipe for causal discovery foundation models
Menghua Wu, Yujia Bao, Regina Barzilay +1
Causal discovery, the task of inferring causal structure from data, has the potential to uncover mechanistic insights from biological experiments, especially those involving pertur…
PROflow: An iterative refinement model for PROTAC-induced structure prediction
Bo Qiang, Wenxian Shi, Yuxuan Song +1
Proteolysis targeting chimeras (PROTACs) are small molecules that trigger the breakdown of traditionally ``undruggable'' proteins by binding simultaneously to their targets and deg…