Pharmaceutical Research

The following examples are indicative of the many use cases of our platform in the pharmaceutical field. We are aware that regulations and compliance plays an important role in this industry and NNaaS will be ready to fully implement ready-to-use pharmaceutical models upon regulatory compliance by states or countries of interest, bear with us for all the upcoming developments and roadmap. X

🧪 Accelerating Drug Discovery with AI

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Problem Statement & Vision

Drug development takes 10+ years and $2.6B per approved drug. Trained specialized models can process more data than a human brain itself and improve over time, especially and with community collaboration: by predicting drug efficacy, toxicity, and patient responses helped by advanced AI models we can increase advancements in curing diseases and groundbreaking discoveries

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Key Use Cases

1. Drug Candidate Screening

• Use Case: Identify promising compounds for diseases ( Alzheimer’s, cancer).

• How NNaaS Works:

  • Data Input: Upload genomic data, chemical structures (SMILES format), or clinical trial datasets.

  • Model Training: Train GNNs (Graph Neural Networks) on protein-ligand binding.

  • Predictions: Rank compounds by efficacy/toxicity ( "Compound #83: 92% binding affinity, low hepatotoxicity").

• Outcome:

  • 50% Faster Screening: Identify top 0.1% candidates in days rather than months.

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2. Clinical Trial Optimization

• Use Case: Predict patient dropout rates and adverse reactions.

• How NNaaS Works:

  • Data Input: Historical trial data + real-time patient vitals (wearables).

  • Model Training: RNNs analyze temporal patient data.

  • Predictions: Flag high-risk cohorts + adjust dosages.

• Outcome:

  • 30% Lower Trial Costs: Reduce patient recruitment failures.

  • Faster FDA Approval: 20% shorter Phase III trials.

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3. Personalized Medicine

• Use Case: Tailor treatments based on patient genomics.

• How NNaaS Works:

  • Data Input: Upload DNA sequencing + electronic health records.

  • Model Training: CNNs identify mutation-drug response links.

  • Predictions: Recommend therapies (e.g., "Patient #441: Responds best to Drug X + Y combo").

• Outcome:

  • 45% Higher Treatment Efficacy in oncology trials.

4. Side Effect Prediction

• Use Case: Forecast drug interactions and adverse effects.

• How NNaaS Works:

  • Data Input: Molecular structures + metabolic pathways.

  • Model Training: Transformers cross-analyze 10M+ PubMed papers.

  • Predictions: Alert on risks (e.g., "Drug A + B: High renal toxicity risk").

• Outcome:

  • Avoid 80% of Late-Stage Failures due to safety issues.

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NNaaS Features for Pharma

• Pre-Built Models:

  • Toxicity predictor, polypharmacy analyzer.

• Collaboration Tools:

  • HIPAA-compliant workspaces for CROs, universities, hospitals.

• Deployment:

  • Export models to lab equipment (e.g., PCR machines).

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Key Takeaways

• Speed to Market: Reduce drug development timing.

• Cost Optimization: Focused budget (a small shift in strategy could save $Millions+ per approved drug).

• Patient Impact: Life-saving therapies reach market faster.