Accelerating Drug Discovery with AI

Leveraging advanced computational tools and machine learning to revolutionize therapeutic development

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Our Services

Comprehensive computational solutions for modern drug discovery

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Omics Data Analysis

Powered by our flagship GATomics platform - #1 ranked in 57.9% of benchmarks. Graph Attention Networks for single-cell multi-omics analysis.

scRNA-seq 160x Faster
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Virtual Screening

BIND and RmsdXNA technologies screen 10B+ compounds. Targets proteins, RNA, and DNA with up to 88% hit rates.

AI-Powered All Targets
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Peptide Design

AI-driven miniprotein and peptide design for "undruggable" targets. Proven 88% success rate in transcription factor targeting.

Miniproteins PPI Inhibitors
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Molecular Dynamics

GROMACS simulations powered by NVIDIA H100 GPUs. 10+ years experience, 1000+ successful simulations.

H100 GPUs μs-scale
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Wet-Lab Validation

Complete experimental validation from cell-based assays to advanced characterization. SPR/BLI, CETSA, flow cytometry and more.

Full Service IC50/EC50
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Our Expert Team

Leading researchers in computational drug discovery

Prof. Mu Yuguang

Prof. Mu Yuguang

NTU Associate Professor

30 years of CADD experience leading the Biomolecular Simulations and Data Analysis Lab

Tan Lai Heng

Tan Lai Heng

4th Year PhD Student

4 years in CADD & ML, 1st author of RmsdXNA

Sun Ao

Sun Ao

1st Year PhD Student

5 years experience in Drug Discovery & Immunology, Wet Lab Director & CTO, 1st author of GATomics

Guan Jia Sheng

Guan Jia Sheng

1st Year PhD Student

4 years in CADD & ML, 2nd author of BIND and GATomics

Wang Zechen

Wang Zechen

PhD

6 years of AIDD, 2 years of enzyme engineering. 3rd prize winner in 2023 Shanghai "Lingyue" AI Drug Screening Challenge

Our Impact

67%
Average Hit Rate
88%
Miniprotein Success
10+
Publications
7
Target Classes

Hit Identifications

Proven success across diverse therapeutic targets

AML Results

Acute Myeloid Leukemia

Identified Gene X as therapeutic target using GATomics. Virtual screening of 2M compounds yielded 2 hits with μM IC50.

Hit Rate: 67% (4/6)
Cancer Protein Target

Cancer Protein Target

Screened 1.2M molecules identifying 4 compounds with mM range Kd values through collaboration with Tsinghua University.

Hit Rate: 21% (4/19)
Z-alpha ADAR1

Z-alpha ADAR1

FDA library screening (2,115 molecules) identified 6 inhibitors of Z-DNA recognition with confirmed activity.

Hit Rate: 60% (6/10)
Chikungunya Virus

Chikungunya Virus

Discovered 2 inhibitors with μM range EC50 values from 10 candidates tested.

Hit Rate: 20% (2/10)
Parkinson's Disease

Parkinson's Disease

4 compounds showed superior inhibition compared to known inhibitors, particularly compounds 11 and 12.

Hit Rate: 33% (4/12)
MALAT1 lncRNA

MALAT1 lncRNA

RNA-targeting screen identified stabilizing and destabilizing compounds with 1.2μM Kd binding affinity.

Hit Rate: 20% (2/10)

Miniprotein Design Excellence

Transcription Factor Targeting

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)
Miniprotein Design Results

Publications

Cutting-edge research in computational drug discovery

Single-Cell Multi-Omics: GATomics (2025)
Protein-Small Molecule Docking: OnionNet, OnionNet2, OnionNet-SFCT, DeepRMSD
Sequence-Based Virtual Screening: BIND
Ligand Language Model: NYAN
Nucleic Acid-Small Molecule Docking: RmsdXNA
Protein-Protein Binding Affinity: ProAffinity-GNN

Ready to Accelerate Your Drug Discovery?

Our team is here to help transform your therapeutic development