Tao Qin

ERNIE-RNA is an RNA language model that incorporates base-pairing restrictions to improve structural representation, achieving state-of-the-art performance in RNA structural and functional predictions.

The paper introduces {micro}Protein, a framework combining deep learning and reinforcement learning to predict and discover high-gain protein mutants using single mutation data.

This paper introduces TamGen, a chemical language model for target-aware molecule generation, and shows it produces high-quality compounds with inhibitory activity against Tuberculosis ClpP protease.

This paper develops a pre-trained antibody language model to improve CDR sequence and structure generation, outperforming previous methods in antigen-specific antibody design.