Lab Leader: Xi Cheng

Lab Name: Laboratory of AI-Driven Glycan Regulation and Drug Discovery

Lab Info:Our lab focuses on integrating artificial intelligence (AI) with molecular simulation methodologies to decode glycan-mediated regulatory mechanisms and pioneer AI-driven drug development platforms.

The research interests include:

1. Algorithm development for molecular interactions. To develop AI and molecular simulation algorithms aimed at modeling multi-scale intermolecular interactions.

2. AI-driven glycan structure-function analysis. To unveil the structure-function relationships of glycans and elucidate their regulatory roles in pathological processes using machine learning methods.

3. Intelligent drug discovery targeting glycanregulation. To establish AI-powered drug screening systems to accelerate therapeutic development for cancer and immune disorders via glycan modulation.

Lab Leader:Shaoyong Lu

Lab Name:Laboratory of Protein Dynamic Structure and Function Prediction and Molecular Discovery

Lab Info：Our labis dedicated to the prediction of protein dynamic structures and functions, as well as the molecular discovery process. Specially, we focus on identifying small molecule binding sites forundruggable proteins and discovering associated molecules.

The research interests include:

1.Prediction of proteindynamic structure and function. This involves employing enhanced sampling techniques and AI-based methodologies tocomprehensively characterize theconformational ensembles of proteins.

2.Molecular discovery. This focuses on identifying small molecules targeting undruggable proteins by leveraging their dynamic conformational ensembles.

Lab Members:

Lab Leader: Haicang Zhang

Lab Name: Laboratory of AI Protein Design

Lab Info：Our lab aims to develop advanced AI algorithms for protein design, drug design and protein complex structure prediction. Our long-term goalisto build the unified generative model for both structure prediction and de novo design across multiple biological molecules, including proteins, nucleic acids (DNA/RNA), and small molecules.

The research interests include:

1. Cutting-Edge AI Methodology

Develop next-generation generative models, reinforcement-learning frameworks, and deep geometric learning methods tailored to life science research.

2. AI-Driven Protein Design

Build generative models for de novo design of therapeutic proteins—antibodies/nanobodies, peptides, and mini-proteins.

3. AI-Driven Protein Structure Prediction

Predict conformational ensembles and interaction interfaces by integrating AI models with physics-based models, thereby accelerating molecular docking and virtual screening.

Lab Members:

Lab Leader: Youwen Zhuang

Lab Name: Laboratory of Molecular Pharmacology and Drug Design

Lab Info：Our laboratory investigates the molecular pharmacology of neurotransmitter receptors and advances structure-based or AI-based drug design, with a primary emphasis on neurotransmitter GPCRs. We focus particularly on receptors fundamental to the onset and progression of neuropsychiatric disorders, including dopamine, opioid, and serotonin receptor systems, etc.

The research interests encompass:

1. Elucidating the molecular basis of precision pharmacology and signal transduction mechanisms of neurotransmitter receptors. The membrane receptors of neurotransmitters such as GPCRs respond to diverse ligands exhibiting distinct pharmacological profiles, such as antagonist, partial agonist, and allosteric modulator activities. We aim to uncover the precise molecular mechanisms underlying these varied pharmacological responses, thereby informing the design of drugs with desired pharmacological activities;

2. Structure-based screening and development of novel therapeutics for neurological disorders;

3. The neuropharmacological mechanisms of neuropsychiatric drugs;

4. Identification and discovery of innovative drug targets for the treatment of neuropsychiatric conditions.

Lab Members: