Virtual Cell Modeling Service

Virtual cell modeling represents an emerging frontier at the intersection of artificial intelligence and biology, aiming to predict quantities such as responses to diverse perturbations quantitatively. However, autonomously building computational models for virtual cells is challenging due to the complexity of biological systems, the heterogeneity of data modalities, and the need for domain-specific expertise across multiple disciplines. CellForge demonstrates how iterative interaction between LLM agents with differing perspectives provides better solutions than directly addressing a modeling challenge.

Understanding complex cellular behavior is critical for advancing drug discovery, biomarker discovery, and therapeutic design. Our virtual cell modeling service empowers you to go beyond static datasets and generate dynamic, predictive models of cell function-quickly and accurately.

Using advanced AI-driven platforms like CellForge, CD ComputaBio integrates single-cell multi-omics data (scRNA-seq, scATAC-seq, proteomics) with computational biology workflows to deliver:

Drug Response Prediction – Simulate cellular outcomes to drug or cytokine treatment.
CRISPR Perturbation Modeling – Forecast knockout effects on gene regulation.
Biomarker Discovery – Identify key molecular signatures driving disease states.
Synthetic Biology Applications – Design and optimize engineered cell functions.

With up to 40% lower prediction error and 20% higher correlation accuracy compared to traditional models, our service provides robust, validated insights to accelerate your R&D.

What is CellForge?

CellForge is an agentic, autonomous virtual cell modeling system that transforms raw single-cell multi-omics data and high-level task descriptions into fully functional and optimized models of cellular behavior. It autonomously generates both the modeling architecture and the executable code needed for training and inference.

virtual cell modeling Figure 1. CellForge: Agentic Design of Virtual Cell Models (Xiangru Tang, et al. 2025)

How CellForge Works?

CellForge employs a sophisticated multi-agent design that integrates three core modules and simulates a collaborative scientific team. The agents in the Design module are separated into experts with differing perspectives and a central moderator, and have to collaboratively exchange solutions until they achieve a reasonable consensus.

Task Analysis

Agents interpret the input dataset (e.g., scRNA-seq, scATAC-seq) and retrieve relevant literature or prior knowledge.

Method Design

A team of domain-specific agents (e.g., data scientists, biologists) debate and propose modeling strategies. A central critic agent moderates this until consensus is reached.

Experiment Execution

Generate production-quality Python code for data preprocessing, model training, and inference pipelines

CellForge's Capabilities

Impressive Accuracy in Single-cell Perturbation Prediction

Use six diverse datasets that encompass gene knockouts, drug treatments, and cytokine stimulations across multiple modalities.

Reduce prediction error (MSE) by up to 40%
Achieve approximately 20% higher correlation metrics (e.g., R², Pearson) compared to traditional modeling approaches

Fully Automated Workflow

From raw data and experimental goals to complete model code—no manual architecture tweaking required.

Cross-Modality Support

Handle scRNA-seq, CITE-seq, scATAC-seq, and more—all within the same framework.

Improved Model Performance

Deliver higher accuracy than task-specific models with minimal manual tuning.

Open & Extensible Codebase

Available on GitHub, making it accessible and adaptable to different use cases

Pharmaceutical & Biotech Applications

Its automated, cross-modal modeling capability significantly lowers technical barriers for labs and accelerates hypothesis testing for drug development and cellular engineering

Application Areas	Function
Drug Response Prediction	Forecast cellular gene expression changes after treatment with drugs or cytokines.
CRISPR Knockout Studies	Model perturbation outcomes in single cells post-CRISPR editing.
Biomarker Identification	Identify molecular signatures for drug response, guiding target selection.
Synthetic Biology & Organoid Design	Enable rational design of cell behaviors and tissue-level modeling.

* The code is publicly available at this URL.

Partner with Us

Let’s discuss how our virtual cell modeling service can support your next project.

* For Research Use Only.

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