Xingang Peng

This paper introduces PocketXMol, a generative foundation model for molecular interactions with protein pockets, achieving state-of-the-art performance in 11 tasks and demonstrating success in designing inhibitors and peptides.

The paper introduces HIL-DD, a human-in-the-loop drug design framework that uses an Equivariant Rectified Flow Model to enable efficient collaboration between human experts and AI for generating drug-like molecules.

This paper introduces Rag2Mol methods for structure-based drug design using retrieval-augmented generation, producing drug candidates with better binding affinities and drug-likeness than existing models.