KnockG

Generate and leverage virtual experimental, diagnostic, and clinical data

KnockGTM, SilicoPharm’s omics foundation model solution, generates and analyzes virtual omics data for experimental, diagnostic, and clinical scenarios based on user-defined conditions. Researchers and clinicians can leverage this to discover novel targets, optimize clinical strategies, and improve bioprocesses such as cell therapy manufacturing.

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Web Application Time-series & condition-specific omics data generation Validated in oncology and neurodegenerative diseases Proven PoC cases with innovative drug companies
Key Capabilities
  • Automated preprocessing of public & uploaded omics data
  • Target perturbation simulation
  • Drug & combination simulation
  • Timepoint expansion (intermediate & future)
  • Pseudo-experimental outputs
  • Ranking & knowledge integration (literature, drugs, patents)

Why KnockG

KnockG is designed with an automated pipeline that enables life science researchers—even without programming expertise—to generate and analyze omics data. By exploring virtual conditions in silico, users can reduce time and cost in experiments and clinical studies while enabling evidence-generating research.

Virtual generation of hard-to-obtain data

Generate data for conditions or samples that are difficult or impossible to obtain due to ethical, practical, or rarity constraints.

Simulation of experimental and clinical outcomes

Generate unseen data such as target perturbation experiments, cohort-specific clinical responses, and process-variable conditions in cell culture and manufacturing.

End-to-end data workflow

Provides preprocessing, condition configuration, analysis, and visualization of generated omics data in a unified workflow.

How It Works

Step 1
Select reference omics data

Use preprocessed dataset, import from public database or upload your own dataset.

Step 2
Generate condition-specific data

Create virtual omics data for conditions not present in training data based on user-defined inputs (e.g., target perturbation, timepoint extension, drug treatment).

Step 3
Analyze and apply results

Visualize generated data and compare conditions to support validation experiments, evidence generation, and optimization of clinical or process conditions.

Designed for AI Ecosystem Integration

KnockG is provided as a web application, API, and MCP, serving as a data generation and augmentation layer that integrates seamlessly with structure-based AI and omics AI systems.

  • Intuitive web interface accessible to non-programmers
  • Seamless integration with external AI systems via API and MCP
  • Supports workflows from target discovery to preclinical and clinical stages alongside structure-based AI
  • Enhances predictive omics AI by providing virtual input data (e.g., future timepoints, cohorts)
Explore Use Cases

Discover applications in target discovery, clinical research, and bioprocess optimization.

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