Research Overview

The Yang Chao Lab develops computational and AI-driven approaches for small molecule drug discovery. Our research integrates computational chemistry, machine learning, structure-based molecular modeling, and ultra-large-scale virtual screening to accelerate the discovery of novel therapeutics.

We focus on developing next-generation computational methods that efficiently explore massive chemical spaces, improve protein–ligand interaction prediction, and enable rational drug design for challenging biological targets.

Current Research Projects

Project 1: AI-Accelerated Ultra-Large Virtual Screening

Overview: We are developing AI-assisted virtual screening methods capable of efficiently screening billions to trillions of compounds from make-on-demand chemical libraries. Our work combines machine learning, docking, and combinatorial chemical space exploration to identify novel small molecule ligands with improved speed and accuracy.

Key Questions:

  • How can machine learning improve large-scale docking efficiency and hit enrichment?
  • How can combinatorial chemical libraries be explored without explicit full enumeration?
  • How can AI models prioritize synthetically accessible and target-specific compounds?

Project 2: Structure-Based Drug Design and Scoring Function Development

Overview: Our lab develops advanced protein–ligand scoring functions and docking methodologies for structure-based inhibitor discovery. We combine empirical modeling, machine learning, and molecular simulations to improve binding affinity prediction and ligand ranking.

Methodology:

  • Molecular docking and rescoring
  • Machine learning-based scoring functions
  • Molecular dynamics simulations
  • Free energy estimation
  • Structure-guided ligand optimization

Publications: See our Publications page

Research Facilities

Our lab is equipped with:

  • High-performance computing (HPC) resources for molecular simulations and virtual screening
  • GPU-accelerated machine learning infrastructure
  • Computational chemistry software for docking, molecular dynamics, and cheminformatics analysis

Funding

Our research is supported by:

  • Shenzhen University of Advanced Technology (SUAT)
  • Collaborative academic and industry partnerships