Leveraging advanced computational tools and machine learning to revolutionize therapeutic development
Start Your ProjectComprehensive computational solutions for modern drug discovery
Powered by our flagship GATomics platform - #1 ranked in 57.9% of benchmarks. Graph Attention Networks for single-cell multi-omics analysis.
BIND and RmsdXNA technologies screen 10B+ compounds. Targets proteins, RNA, and DNA with up to 88% hit rates.
AI-driven miniprotein and peptide design for "undruggable" targets. Proven 88% success rate in transcription factor targeting.
GROMACS simulations powered by NVIDIA H100 GPUs. 10+ years experience, 1000+ successful simulations.
Complete experimental validation from cell-based assays to advanced characterization. SPR/BLI, CETSA, flow cytometry and more.
CataPro - AI-powered enzyme kinetic parameter prediction. Accurately predicts kcat, Km, and catalytic efficiency for enzyme discovery and modification.
Leading researchers in computational drug discovery
NTU Associate Professor
30 years of CADD experience leading the Biomolecular Simulations and Data Analysis Lab
4th Year PhD Student
4 years in CADD & ML, 1st author of RmsdXNA
1st Year PhD Student
5 years experience in Drug Discovery & Immunology, Wet Lab Director & CTO, 1st author of GATomics
1st Year PhD Student
4 years in CADD & ML, 2nd author of BIND and GATomics
PhD
6 years of AIDD, 2 years of enzyme engineering. 3rd prize winner in 2023 Shanghai "Lingyue" AI Drug Screening Challenge
Proven success across diverse therapeutic targets
Identified Gene X as therapeutic target using GATomics. Virtual screening of 2M compounds yielded 2 hits with μM IC50.
Hit Rate: 67% (4/6)Screened 1.2M molecules identifying 4 compounds with mM range Kd values through collaboration with Tsinghua University.
Hit Rate: 21% (4/19)FDA library screening (2,115 molecules) identified 6 inhibitors of Z-DNA recognition with confirmed activity.
Hit Rate: 60% (6/10)Discovered 2 inhibitors with μM range EC50 values from 10 candidates tested.
Hit Rate: 20% (2/10)4 compounds showed superior inhibition compared to known inhibitors, particularly compounds 11 and 12.
Hit Rate: 33% (4/12)RNA-targeting screen identified stabilizing and destabilizing compounds with 1.2μM Kd binding affinity.
Hit Rate: 20% (2/10)Using advanced AI tools for sequence generation and filtering, we designed miniproteins to target transcription factor proteins with exceptional success.
Hit Rate: 88% (7/8)Cutting-edge research in computational drug discovery
Our team is here to help transform your therapeutic development