At the core of our innovation is the CYTOCAST DIGITAL TWIN Platform™, a cutting-edge high-performance computing platform that leverages a particle-based stochastic simulation algorithm to replicate the intricate interactions of proteins within a virtual cell. By modeling the complexation, decomplexation, and diffusion of proteins within cells, their compartments, and membranes, our simulator delivers both qualitative and quantitative insights into the cellular complexome. 

Our approach is grounded in the principle that most biological functions are carried out by protein complexes. The cell simulator enables us to predict patient responses to treatments by simulating protein complex formation across multiple tissues, providing valuable data for personalized medicine. 

A key aspect of our platform is the integration of diverse drug data from publicly available databases into our proteome-wide simulation pipeline. By simulating drug perturbations, we incorporate multiomics data specific to both the drug and the targeted cell type. These simulations are analyzed statistically to identify significant changes in protein complex abundance and structure caused by perturbations. Importantly, these changes are correlated with potential off-target effects and side effects, making off-target prediction and safety assessment a central focus of our platform.  

The CYTOCAST DIGITAL TWIN Platform™ – Workflow Overview 

The diagram illustrates how the CYTOCAST'S DIGITAL TWIN Platform™ processes drug candidate information to generate actionable insights for customers. 

1. Input: Drug Candidate Information 

• The process begins with the customer providing drug candidate information, typically in the form of a SMILES code. 
• Cytocast integrates this input with partner data, incorporating various biological datasets. 

2. Data Integration & Analysis 

The Cytocast platform processes the input by leveraging: 

• Drug target identification – Determining which proteins the drug is expected to bind to. 
• Proteomics data – Understanding protein expression levels and interactions. 
• Protein interaction networks – Mapping how proteins interact with each other in different cellular environments. 
• Complex formation pathways – Studying how protein complexes are assembled and perturbed by drug interactions. 

3. Predictive Modeling with AI-Powered Tools 

The Cytocast platform uses three core AI-driven components to analyze drug behavior: 

• Cytocast Off-Target Predictor (AI-powered) – Predicts off-target interactions, identifying unintended protein bindings that may lead to adverse effects. 
• CYTOCAST DIGITAL TWIN Cell™ – Simulates protein complex perturbations, modeling how the drug affects cellular environments at a molecular level. 
• Cytocast (Side) Effect Predictor (AI-powered) – Predicts potential side effects and broader drug effects, helping researchers assess safety risks. 

4. Cytocast Report as an Output

The platform generates a comprehensive interactive report, which is delivered to the customer. This report includes: 

• Predicted off-target interactions – Identifying unintended binding sites. 
• Protein complex perturbations – Showing how the drug alters molecular networks. 
• Predicted effects and side effects – Highlighting potential risks for further investigation. 

Why It Matters 

By leveraging Cytocast’s AI-powered predictive modeling, researchers and pharmaceutical companies can evaluate drug safety and efficacy early—before investing in costly experiments or clinical trials. This accelerates drug discovery, reduces development risks, and helps refine molecular designs for safer and more effective therapeutics.