AI + Computational Chemistry

AI-Assisted Drug Design Laboratory

We use artificial intelligence and computational chemistry to accelerate drug discovery and design. Deep learning, molecular simulation, and large-scale data analysis help move promising ideas toward validated candidates.

Open TAME-VS Read Docs

Research

Research Directions

We build reusable drug-design workflows around target identification, molecular representation, virtual screening, and experimental validation.

AI-Powered Drug Design

Leverage deep learning and machine learning to accelerate drug candidate screening and optimization, improving efficiency and accuracy.

Computational Chemistry

Understand drug-target interaction mechanisms through molecular simulation and quantum chemistry calculations.

Computational Biology

Integrate multi-omics data and biological network analysis to systematically reveal disease mechanisms and drug action pathways.

Medicinal Chemistry

Combine computational and experimental approaches to design and optimize lead compounds with high activity and favorable pharmacokinetic properties.

Targeted Drug Discovery

Precisely screen and design targeted therapeutics based on target structure and functional characteristics.

Team Members

An interdisciplinary team across artificial intelligence, medicinal chemistry, structural biology, and platform engineering.

Ming Li

Principal Investigator

Qian Wang

Algorithm Researcher

Jie Zhang

Computational Chemist

Yu Chen

Platform Engineer

Platform Access

Start a traceable virtual screening task

Open TAME-VS