Rooted in the Core Concerns of Every Individual

Illuminating Blind Spots in Healthcare. Confronting the Incurable.

We concentrate on diseases and suffering that conventional wisdom deems"incurable"or"extremely refractory". There are no standard answers here—only our deepest response to human suffering.

We bring together cutting-edge AI and interdisciplinary expertise to probe the heart of every complex case, re-evaluate every therapeutic possibility, and forge entirely new pathways for seemingly intractable challenges.

"Our purpose is to become, after the patient's final resort,
that indispensable light—and a solid new beginning."

RitanAI

Intelligent Operating System for Biomedical R&D

Addressing the dual challenges of data silos and biological complexity, reshaping the R&D paradigm from target discovery through clinical validation

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Unified Intelligent Foundation

Natively integrates 14+ leading international bioinformatics platforms, seamlessly connecting multi-scale data across genomics, proteomics, imaging, and beyond—building traceable, computable biological data assets

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World Model Engine

Billion-parameter life science foundation models integrating causal inference and federated learning, simulating in vivo biological processes to deliver interpretable predictive decisions that transcend conventional correlation analysis

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End-to-End Translation Acceleration

Supporting closed-loop wet-dry experimental iteration, compressing the translational pathway from early hit identification to preclinical candidate (PCC), delivering a regulatory science-friendly evidentiary framework

BiomedicineAIR&D platform

Core Platform Capabilities

Integrating multi-omics data analysis with AI-driven drug discovery to provide comprehensive support across the biomedical R&D spectrum

Multi-omics Data Integration

IntegrationGenomics、Transcriptomics、Proteomics、Metabolomics和Spatial OmicsData，Providing comprehensive data analysis perspectives

  • Supports multiple data formats and sources
  • Automated data preprocessing and standardisation
  • Efficient data storage and management

Tool Integration Hub

Integrating 14+ mainstream bioinformatics tools with unified API interfaces and user experience, streamlining analytical workflows

  • Genomics Analysis Tools
  • Transcriptomics & Proteomics Analysis
  • Metabolomics & Spatial Omics Analysis

AI-Driven Engine

Based on machine learning and deep learningAI Engine，SupportMulti-omicsData analysis and drug discovery

  • Target Prediction & Virtual Screening
  • Molecular Design & Optimisation
  • Clinical Trial Design & Optimisation

Visualisation Analysis

Rich data visualisation and interactive exploration capabilities to illuminate complex biological data

  • Basic charts and advanced visualisation
  • Interactive Data Exploration
  • Automated report generation

Collaboration & Knowledge Management

Enabling team collaboration and knowledge sharing to enhance R&D efficiency and result reusability

  • Project management and tracking
  • Team collaboration and data sharing
  • Knowledge management and sharing

Scalable Architecture

Microservices-based architecture supporting functional expansion and bespoke development

  • Microservices architecture design
  • API interfaces and plugin system
  • Bespoke development support

World Model: Deep AI-Biomedicine Integration

Our world model technology deeply integrates AI with biomedical domain knowledge, constructing a dynamically learning intelligent system—theMultimodal Life Science World Model。

Vast Biomedical Knowledge

Integrating authoritative global databases and literature resources

Continuous Knowledge Updates

Automatically synchronising latest research findings daily

High-Precision Prediction

Significantly enhancing prediction accuracy and reliability

R&D Efficiency Enhancement

Substantially compressing R&D timelines and reducing costs

Multimodal Life Science World Model

Biomedical Knowledge Graph

Constructing a comprehensive biomedical knowledge graph integrating multi-dimensional entities—genes, proteins, compounds, diseases—and their complex relational networks, providing systematic domain knowledge support for AI

Continuous Learning & Evolution

The world model continuously learns from vast research literature, clinical trial data, and real-time monitoring, automatically updating and refining its knowledge base to ensure cutting-edge accuracy

Multimodal Data Fusion

Fusing genomics, transcriptomics, proteomics, metabolomics, imaging omics, and other data modalities to construct a comprehensive biomedical world model delivering more accurate predictions and analyses

Virtual Experiments & Prediction

Conducting virtual experiments and predictions based on the world model，Significantly improving prediction accuracy, substantially accelerating drug discovery, effectively reducing R&D costs, and enhancing success rates

Multimodal Molecular Design

Universal molecular design world model enabling unified design of multi-form molecules—proteins, nucleic acids, small molecules—significantly improving design efficiency and compressing computational design cycles

World Model Core Capabilities

Intelligent knowledge retrieval

Intelligent biomedical knowledge retrieval based on natural language understanding，Rapidly acquiring relevant research findings and data

Scientific discovery assistance

Assisting researchers in discovering new scientific hypotheses and research directions，Accelerating the scientific discovery process

Predictive analysis

Predictive analysis based on multimodal data，Predict drug efficacy、Disease risk and treatment response

Experimental design optimisation

Optimising experimental design plans，Improving experimental efficiency and success rates，Reduce R&D costs

Multimodal Molecular Design

Based on world model technology，Enabling unified design of multi-form molecules such as proteins, nucleic acids, and small molecules

Multi-omics Data Visualisation

Explore complex biological data relationships through interactive 3D visualisation

Interactive Data Exploration

Through 3D visualisation technology, intuitively display complex relationships between genomics, transcriptomics, proteomics, and metabolomics—helping researchers discover novel biomarkers and drug targets.

Integrating 14+ Global Mainstream Bioinformatics Platforms

Natively integrating leading international bioinformatics platforms, seamlessly breaking through multi-omics data barriers

Spatial Omics Analysis Tools

Spatial Omics

Comprehensive toolkit for spatial transcriptomics data analysis, supporting complete workflows from raw data processing to advanced visualisation

Spatial Data Framework

Spatial Data

Unified framework for storing and processing spatial biology data, supporting multiple spatial data types and formats

Multi-omics Integration Platform

Multi-omics

Platform for integrating and analysing multi-omics data, supporting joint analysis of genomics, transcriptomics, proteomics, and metabolomics data

Bioinformatics Cloud Platform

Cloud Platform

Bioinformatics cloud platform providing rich analysis tools and workflows, supporting one-stop analysis from data upload to result download

Interactive Visualisation Tools

Visualisation

Shiny-based interactive multi-omics data analysis and visualisation tools, supporting multiple chart types and data exploration capabilities

Multi-omics Analysis Tools

Multi-omics

R package for multi-omics data integration and analysis, supporting variable selection, classification, prediction, and other analytical functions

Multi-omics Factor Analysis

Factor Analysis

Multi-omics factor analysis tool，For extracting shared and specific sources of variation from multiple omics datasets

Omics Association Analysis Tools

Association Analysis

Tools for analysing associations between genomics and metabolomics data, supporting identification of potential biomarkers and drug targets

Metabolomics Analysis Platform

Metabolomics

Metabolomics data analysis platform supporting metabolite identification, quantitative analysis, pathway analysis, and biomarker discovery

Metabolite Tracing Analysis

Metabolite Tracing

Tools for metabolite source analysis, supporting identification and quantitative analysis of metabolite sources in biological samples

Genomics Analysis Tools

Genomics

Genomics analysis tools supporting DNA sequencing data processing, gene expression analysis, variant detection, and annotation

Gene Sequencing Analysis Platform

Association Analysis

Cloud platform for analysing genomics data, supporting gene expression analysis, variant detection, and association analysis

CAT Bridge

Multi-omics Association

Compounds and Transcripts Bridge—efficient multi-omics data analysis tool integrating 7 correlation coefficient calculations and causal relationship modelling methods, particularly adept at gene-metabolite association mining

Universal Molecular Design Tool

Molecular Design

Universal molecular design world model，Enabling unified design of multi-form molecules such as proteins, nucleic acids, and small molecules，Design efficiency improved by nearly50times

AI-Driven Engine

Machine learning and deep learning-powered AI engine enhancing biomedical industry efficiency

AI-DrivenDrug discovery

AI-Driven Biomedical Industry Efficiency Enhancement

Our AI engine integrates advanced machine learning and deep learning algorithms capable of analysing massive biological data, predicting drug targets, designing novel molecular structures, and optimising clinical trial protocols—comprehensively enhancing biomedical industry efficiency.

Target Prediction

Leveraging deep learning algorithms to analyse multi-omics data, predict potential drug targets, and enhance target discovery efficiency and accuracy

Virtual screening

Employing machine learning models for virtual screening of large-scale compound libraries, identifying candidate compounds with potential activity

Multimodal Molecular Design

Universal molecular design world model enabling unified design of multi-form molecules—proteins, nucleic acids, small molecules—significantly improving design efficiency

Clinical Trial Design

AI-assisted clinical trial protocol design and optimisation, improving clinical trial success rates and efficiency

AI-Driven R&D Efficiency Enhancement

R&D Cycle Optimisation

Through AI-assisted target discovery, virtual screening, and molecular design, significantly compressing drug R&D timelines. Traditional methods require years of R&D processes; AI technology can accomplish them in significantly less time, substantially improving R&D efficiency.

R&D Success Rate Enhancement

AI models significantly enhance drug R&D success rates through multi-dimensional data analysis and prediction. Through precise prediction and intelligent decision-making, effectively reducing R&D risks and costs, improving project success rates.

AI Model Core Capabilities

Target Prediction

AIModel

Leveraging deep learning algorithms to analyse multi-omics data, accurately predict potential drug targets, significantly improving target discovery accuracy and efficiency

Virtual screening

AIModel

Employing machine learning models for rapid virtual screening of large-scale compound libraries, substantially improving screening efficiency, quickly identifying candidate compounds with potential activity

Clinical Trial Optimisation

AIModel

AI-assisted clinical trial protocol design and patient recruitment optimisation, improving clinical trial success rates and execution efficiency

Intuitive & User-Friendly Interface

Carefully designed user interface simplifying complex bioinformatics analysis workflows, improving research efficiency

PlatformInterface
Data analysis interface
DataVisualisationInterface

Design Philosophy

Our user interface design follows a "user-centred" philosophy—through intuitive layout and interaction, making complex bioinformatics analysis simple and accessible. Whether you are an experienced bioinformatician or a new researcher, you can quickly get up to speed and efficiently complete analysis tasks.

Modular Layout

Employing modular design, breaking down complex analysis workflows into simple steps, guiding users through the entire analysis process

InteractiveVisualisation

RichInteractive Visualisation Tools，Helping users understand complex data relationships and analysis results

Customisable workflows

Supporting users in customising analysis workflows according to their needs，Improving analysis efficiency and flexibility

Responsive Design

Fully responsive design, supporting use across various devices, enabling data analysis anytime, anywhere

Main Interface Modules

Dashboard

Intuitively displaying project progress, data overview, and recent analysis results, helping users quickly understand project status

Data Management

Centrally managing all data files, supporting data upload, download, preview, and organisation, facilitating data management and sharing

Analysis Tools

Integrating 12+ bioinformatics tools, providing a unified user interface, simplifying analysis workflows

Visualisation Centre

Providing rich data visualisation tools, supporting interactive data exploration and result presentation

AI Laboratory

Providing AI model training, prediction, and optimisation functions, supporting custom models and parameters

Collaboration Space

Supporting team collaboration and knowledge sharing, providing discussion, commenting, and feedback functions

Biomedical R&D Workflow

AI-driven biomedical R&D workflow, from basic research to clinical application, fully accelerated

Target Discovery

Leveraging AI to analyse multi-omics data, identifying potential drug targets

Virtual screening

Performing virtual screening on large-scale compound libraries，Identify potentially active compounds

Molecular Design

AI-based molecular design and optimisation, creating novel drug molecules

Preclinical Research

AI-assisted preclinical research design and optimisation

Clinical trial

AI-optimised clinical trial design and patient recruitment

Detailed Workflow

1. Target Discovery & Validation

AI-Driven

Leveraging AI to analyse multi-omics data, identifying potential drug targets, and validating their efficacy and safety through experiments.

Key Technologies
Multi-omics Data Integration AI Target Prediction Network Analysis Functional Validation

2. Virtual Screening & Molecular Design

AI-Driven

Performing virtual screening on large-scale compound libraries, identifying potentially active compounds, and using AI to design novel molecular structures.

Key Technologies
Molecular docking Machine Learning Models Generative Models Molecular Dynamics

3. Preclinical Research

AI-Assisted

Conducting in vitro and in vivo experiments to evaluate drug efficacy, safety, and pharmacokinetic characteristics.

Key Technologies
Cell Models Animal Models AI Experimental Design Pharmacokinetic Analysis

4. Clinical trial

AI-Optimised

Designing and conducting clinical trials to evaluate drug safety and efficacy in humans.

Key Technologies
Trial Design Optimisation Patient Recruitment Real-time Data Analysis Risk Prediction

5. Post-Market Surveillance

AI Monitoring

After drug launch, continuously monitoring safety and efficacy, collecting real-world data.

Key Technologies
Adverse Event Monitoring Real-World Data Analysis AI Early Warning System Drug Repositioning