Yuan Zhang

The paper constructs an IID-specific knowledge graph from biomedical literature to support disease ontology refinement and drug repurposing.

The paper constructs a comprehensive biomedical knowledge graph (iKraph) using PubMed abstracts and integrates it with public databases to enable AI-powered knowledge discovery, demonstrating its utility in drug repurposing.

DisDock predicts protein-metal docking using a physics-driven deep learning method, achieving higher accuracy than existing approaches for various metal ions.

ProDCoNN-server predicts and designs protein sequences from 3D structures using deep learning models, offering fast and accurate results with visualization tools.

This study estimates the designability of real proteins using a deep neural network model and validates the results with AlphaFold2 and RoseTTAFold.

This paper introduces two deep learning models for protein loop modeling that improve accuracy without relying on multiple sequence alignments.

The paper applies AlphaFold to predict stability changes of engineered proteins and estimates the designability of a real protein structure.