Hanqun Cao

SubcellPert benchmarks subcellular perturbation prediction using imaging data, showing Gaussian transport achieves low spatial error in out-of-distribution datasets.

ApexFold predicts peptide secondary structure changes across environments using sequence and physicochemical descriptors, outperforming static predictors.

This paper introduces DuetRNA, a dual-frame flow matching model for RNA co-design that improves sequence-structure generation by jointly modeling base-and-sugar frames.

This paper introduces the EDEN family of foundation models for AI-programmable therapeutic design, demonstrating their ability to generate functional gene insertions, antibiotic peptides, and synthetic microbiomes.

This paper introduces a deep reinforcement learning framework for designing new antibiotics, producing potent peptides with broad-spectrum activity against bacteria.

InstructPLM aligns a protein language model with structure instructions to generate diverse proteins, improving performance over existing models in sequence recovery and activity.