Engineering Biological Confidence: How Single-Cell Validation Fits into the AlgorithmicRx Platform

At AlgorithmicRx, our mission has always been clear: accelerate the discovery of transformative therapies for rare diseases by combining deep biology with artificial intelligence.

As our platform advances into the MVP stage, we are continuing to refine not only how we identify therapeutic targets, but also how we build biological confidence around them before moving into downstream drug design.

Beyond Target Discovery

Our AI platform integrates diverse biological data—including genomics, transcriptomics, proteomics, biological pathways, and scientific literature—to prioritize therapeutic targets through biologically constrained machine learning and network biology.

This approach has enabled us to identify a promising lead target candidate within our current development pipeline.

However, identifying a target is only one milestone.

The next challenge is determining whether that target is truly active in the specific cell populations driving disease progression.

Why Single-Cell Biology Matters

Diseases such as Duchenne Muscular Dystrophy involve complex interactions among multiple cell types, including muscle fibers, satellite cells, fibro-adipogenic progenitors, immune cells, and fibroblasts. Traditional bulk sequencing averages signals across all of these cells, often masking the underlying biology.

To address this, AlgorithmicRx is expanding its platform architecture to incorporate single-cell RNA sequencing (scRNA-seq) as a biological validation layer.

Rather than serving as another discovery dataset, single-cell analysis helps answer critical questions after target prioritization:

• Is the target expressed in the disease-driving cell populations?

• How does target expression change throughout disease progression?

• Which cellular pathways are disrupted?

• How do different cell populations communicate with one another?

• Which biomarkers can be used to monitor therapeutic response?

This additional layer of biological resolution helps strengthen confidence in target selection before advancing to computational drug design.

Integrating Structural AI

Following target prioritization and single-cell validation, our structural biology workflow leverages state-of-the-art protein modeling platforms, including AlphaFold 3 (AF3), OpenFold, and Boltz2, to evaluate protein structure, binding interfaces, and molecular interactions.

These structural insights feed directly into virtual screening and lead optimization, enabling a more informed and mechanism-driven approach to candidate selection.

An End-to-End Biology-First AI Native Pipeline

Our evolving platform now follows a comprehensive workflow:

Patient & Public Datasets → Multi-Omics Integration → Biologically Constrained AI → Target Prioritization → Lead Target Candidate → Structural Biology (AF3, OpenFold & Boltz2) + Single-Cell Validation → Target Confidence Scoring → Virtual Screening → Lead Optimization → In-silico Safety & Off-target Prediction → Experimental Validation → Preclinical Development

By integrating computational biology, structural AI, and single-cell biology, we aim to reduce uncertainty earlier in the drug development process while focusing resources on the most biologically relevant therapeutic opportunities.

Looking Ahead

This evolution reflects our long-term vision of building an AI-powered, biology-first drug discovery platform capable of identifying, validating, and optimizing therapies for rare diseases with greater precision and efficiency.

While our initial focus remains Duchenne Muscular Dystrophy, the underlying platform is designed to be extensible across other rare and genetically driven disorders.

At AlgorithmicRx, we believe the future of drug discovery will not be driven by AI alone—it will be powered by AI working hand in hand with deep biological understanding.

Deep Biology. Bolder Solutions. Better Tomorrows.