Jianmin Wang

NACraft generates high-confidence nucleic-acid aptamers for diverse protein targets using a programmatic framework with structure-model feedback, outperforming existing methods in several evaluations.

This paper constructs a mutation-disease dataset and proposes CGM to predict mutation-disease associations, identifying 22 unknown protein interaction pairs.

This paper introduces GENiPPI, a molecular generative framework for PPI interfaces, which generates novel compounds targeting PPIs and demonstrates its effectiveness in capturing interface-molecule relationships.

This study uses a transformer-based generative model to generate novel protein-protein complex conformations from molecular dynamics simulations, enhancing conformational exploration.

The paper introduces ACGDC, a deep learning model that generates synthetic single-cell multi-omics samples to aid in brain disease research.