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Microsoft Fabric Thursdays �Expert Series - 2025

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Content adapted from Microsoft AI Skills Fest under CC-BY 4.0 License

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Microsoft Fabric Thursdays: Expert Series (2025) by:

Microsoft Fabric User Group: Uttar Pradesh Power BI Club

Quantum AI in Pharma: Accelerating Precision Drug Discovery with Next-Gen Tech

Kartheek Sankranthi

Long Island University, Brooklyn

#Expert Series

Kartheek Sankranthi

Long Island University, Brooklyn

@ [https://www.linkedin.com/in/kartheek-sankranthi/]

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Interests/Achievements

• Built AI-driven supply chain and quality monitoring systems.
• Developed scalable web and API solutions with React and Node.js.
• Designed predictive analytics dashboards for decision-making.
• Led automation projects improving efficiency and security.

Agenda

1. Quantum Advantage in Drug Discovery
2. Industry Use Cases and Success Stories
3. Implementation Challenges
4. Strategic Roadmap and Future Outlook

The Pharmaceutical Innovation Challenge

The traditional drug discovery �pipeline suffers from massive inefficiencies, with billions invested in candidates that ultimately fail. These challenges create:

• Higher healthcare costs passed to patients and insurers
• Delayed access to potentially life-saving treatments
• Risk-averse R&D strategies favoring incremental improvements
• Limited exploration of treatments for rare diseases���

13.8% Success Rate�Only 13.8% of drug candidates successfully �transition from Phase I trials to regulatory �approval (Wong et al., 2019)��15 yrs Timeline�Average drug development timeline from �discovery to market approval��

$2.8B Average Cost�Per approved drug, according to DiMasi et al. �(2016

Quantum Computing: A Paradigm Shift

Superposition�Enables simultaneous exploration of multiple molecular configurations, exponentially accelerating conformational analysis compared to classical methods

Entanglement�Allows precise modeling of complex electron correlations critical for understanding drug-target binding mechanisms and specificity

Quantum Algorithms�Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) have achieved chemical accuracy for small molecules on existing hardware

Proven Quantum Advantages in Molecular Simulation

2019�Nature Physics publication demonstrates quantum VQE algorithm achieving chemical accuracy for hydrogen �molecules, outperforming classical computation

2021�Physical Review X study shows quantum algorithms successfully modeling protein-ligand interactions �for small peptides

2022�First commercial quantum-enhanced virtual screening platform identifies novel binding candidates for SARS-CoV-2 protease inhibitors

2023�Hybrid quantum-classical models achieve 87% accuracy in predicting drug efficacy for cancer therapies, reducing screening costs by 22%

Quantum simulations consistently demonstrate superior accuracy in predicting molecular properties crucial for drug discovery

Industry Momentum: Strategic Quantum Partnerships

• Research Collaboration
• Technology Development
• Partnerships Established
• Clinical Applications
• Commercial Deployment

These strategic alliances are driving rapid adoption of quantum technologies across the pharmaceutical value chain, from target identification to clinical trial optimization. Early adopters are positioning themselves to capture significant competitive advantages as quantum hardware capabilities continue to scale.

Projected Impact on Drug Discovery Pipeline

Cost Reduction

Nature Reviews Drug Discovery projects:�

• 15-30% reduction in overall R&D costs
• Up to 40% reduction in early-stage discovery timelines
• 25% improvement in candidate success rates

Most significant gains come from improved prediction accuracy in target identification and lead optimization phases, reducing costly late-stage failures

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Quantum-Enhanced Personalized Medicine

Genomic Data Analysis

Quantum algorithms process vast genomic datasets to identify subtle patterns in drug response across different genetic profiles

Pharmacogenomic Modeling

Quantum-classical hybrid models predict individual drug responses with 3.5x higher accuracy than traditional methods

Tailored Therapies

Patient-specific treatment protocols that maximize efficacy while minimizing adverse drug reactions

Quantum Hardware Roadmap

With 100+ qubit systems anticipated within 3-5 years, pharmaceutical companies should begin building quantum-ready workflows now to capitalize on each advancement in hardware capability

Implementation Challenges

Technical Barriers

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• Error correction requirements for pharmaceutical applications
• Scaling algorithms beyond toy problems to real drug candidates
• Integrating quantum modules with existing discovery pipelines

Organizational Hurdles

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• Talent acquisition in competitive quantum computing market
• Cultural resistance to adopting unfamiliar technologies
• ROI justification for early-stage quantum investments

Strategic Recommendations for Pharmaceutical Leaders

Companies that implement these strategies will be positioned to capture the full value of quantum computing as the technology matures, gaining first-mover advantages in precision drug discovery

The Time to Act is Now

"Quantum computing for drug discovery isn't just about incremental gains - it represents a fundamental reimagining of how we identify, optimize, and deliver life-saving therapies."

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- Dr. Jennifer Harris, Quantum Biosciences Institute

Next Steps

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• Schedule a quantum readiness assessment
• Identify high-value use cases within your pipeline
• Develop a 3-year quantum implementation roadmap
• Explore partnership opportunities with quantum providers

Let's dive into conversations:

Connect On LinkedIn with Expert; Kartheek Sankranthi

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LinkedIn URL [https://www.linkedin.com/in/kartheek-sankranthi/]

Microsoft Fabric Expert Series 2025

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Content adapted from Microsoft AI Skills Fest under CC-BY 4.0 License