23andMe, Artificial Intelligence and Quantum Computing - Draft

Here’s a clear, practical way to think about 23andMe at the intersection of Artificial Intelligence (AI) and Quantum Computing—what’s real today, and what’s plausible longer-term.

1. 23andMe, Artificial Intelligence (AI) and Quantum Computing 

AI is already core to how many companies like 23andMe operate.

A. Genetic data analysis

23andMe has genotypes for millions of customers, creating one of the world’s largest human genetic datasets.

AI  and Machine Learning is used to:

• Identify associations between genes and traits (GWAS studies)
• Detect patterns linked to disease risk (e.g., Parkinson’s, Alzheimer’s, diabetes)
• Improve polygenic risk scores (PRS), which combine many small genetic effects
• Reduce noise and bias in genetic data

B. Drug discovery (23andMe and Therapeutics)

Before selling its therapeutics division assets, 23andMe used AI to:

• Match genetic variants to drug targets
• Predict which targets are more likely to succeed clinically
• Prioritize targets with human genetic validation (lower failure risk)

This approach is now standard across biotech (also used by companies like Recursion, Insilico, Deep Genomics).

C. Personalized health insights

AI helps:

• Translate raw DNA variants into consumer-friendly reports
• Continuously update interpretations as new research emerges
• Stratify populations by genetic risk rather than one-size-fits-all medicine

Bottom line with AI:

AI is indispensable for extracting value from large-scale genetic data, and 23andMe would not function without it.

2.  23andMe and Quantum Computing 

Quantum computing is not currently used in production by 23andMe—but it is relevant over the long-term.

A. Where quantum computing could matter for Genomics Data Analysis 

Quantum computing could eventually help with:

1. Combinatorial genetics

• Human traits involve thousands of interacting genes
• Classical computers struggle with the full combinatorial explosion
• Quantum algorithms may explore these interaction spaces more efficiently

2. Protein folding and molecular simulation

• Understanding how genetic variants alter proteins
• Quantum simulation could improve:
  
• Drug–protein binding predictions
• Effects of mutations at the quantum chemistry level

3. Optimization problems

• Identifying optimal drug targets across massive genetic networks
• Matching genetic subpopulations to treatments

B. Timeline reality check