We revolutionarily integrate information technology, artificial intelligence technology, and supercomputing into

the field of biomedical science and drug discovery, and effectively utilize multimodal big data from

real-world patients (e.g. multi-omics data, phenotypic data and big knowledge from more than 40 million

biomedical articles to establish simulation models of causal mechanisms.


(Topic-wise inference engine of massive biomedical literatures)

※Biomedical literature processing algorithms
※Large-scale biomedical knowledge AI platform

We're using advanced algorithms and AI technology to construct a super brain of biomedical knowledge. Our platform is able to perceive the relevant literature of target drugs or target diseases, and transfer knowledge from other fields to the target field. This allows us to find out new mechanisms of action and new indications for target drugs, as well as new pathogenesis and new indications for target diseases. Our technology has the potential to discover new and effective medicine, and ultimately improve patient outcomes.
A database with more than 1 million knowledge models


(Damage Assessment of Genomic ShotGun)

※Genomic Shotgun Damage Assessment Algorithm
※AI platform for quantitative assessment of cell function

We're proud to offer the world's first and exclusive genome interpretation AI tool invented by Phil Rivers.Using all the variation information in the individual genome, our tool is able to describe the individualized cellular function and activity profile of signaling pathways (APSPs), effectively correlating individual genotypes with physiological and pathological phenotypes. What sets our tool apart is that it only requires a small sample size to identify the key characteristics of a specific disease. This allows us to support the screening of new indications and dominant populations, and to predict efficacy with great certainty.
More than 500 APSP models


(Digital Patient Metaverse)

※Pan-cancer functional digital patient database
※Virtual platform for studying MOAs

We're using advanced AI technology to implement APSPs mapping of germline genome information and tumor genome information of pan-cancer patients.This allows us to conduct virtual clinical trials using MOA models, and to count the proportion of patients responding to various drug mechanisms under different cancer types. Our technology has the potential to predict indications and patient portraits for various drugs in advance, which can help improve the accuracy and effectiveness of drug research and development.
More than 20000 cancer patients
  • Precise Simulation of Biological Mechanisms
    Simulation is not a black box
    Quantitative simulation of signalling pathway activities
    Intuitively describe etiology and pathology
  • Broad Coverage of Diseases and Drugs
    30+ pan-solid tumors
    100+ pathological types
    Autoimmune diseases
    Small-molecule inhibitors or agonists,
    Monoclonal antibodies Bispecific antibodies,
    Fusion proteins, Cell therapy
  • In-depth Understanding of Diseases and Targets
    Etiology, Pathology,
    Characteristics of susceptible population,
    Diagnosis, Treatment, Prognosis, Follow-up
    Biological mechanism, Indications, Preclinical studies, Drug efficacy and prognosis, Lack of significant efficacy, Improvement of drug resistance, Biomarker for efficacy and screening drug-sensitive patients, Genomics evidence, Adverse effects, Pharmacokinetics
  • High Return on Investment (ROI)
    The MOAs and virtual clinical trials simulation of a specific drug can be performed using data with a small sample size.
    The results directly reflect the underlying biological mechanism and can be seamlessly followed by wet lab validation.