[Volume 30. Kvantify: Quantum Computing for Drug Discovery - Danish Company Bridging Classical and Quantum Computing for Molecular Simulation]

Executive Overview

Founded: 2022

Headquarters: Copenhagen, Denmark (Additional offices: Aarhus, London)

Website: https://www.kvantify.com

Core Business: Quantum and high-performance computing software for drug discovery and molecular simulation

Total Funding: €10 million Seed Round (July 2024)

Founders:

• Hans Henrik Knudsen (CEO) - Nuclear physicist, former McKinsey engagement manager, former KMD director

• Nikolaj Zinner (Co-founder) - Quantum computing researcher

• Allan Grønlund (Co-founder) - Computer science background

Lead Investors:

• Dreamcraft (Danish VC)

• Lundbeckfonden BioCapital

• 2degrees, Redstone VC, 2xN, EIFO

Chairman: Dr. Jörg Weiser (appointed January 2025)

Team: Approximately 60-67 employees including 35+ PhDs and 7 professors

Key Technology:

• Koffee platform for computational drug discovery

• FAST-VQE quantum algorithm for quantum chemistry

• Hybrid classical-quantum computing architecture

Products:

• Koffee Unbinding Kinetics (launched March 2024)

• Koffee Binding Affinity (launched October 2024)

Partnerships: AWS Braket, Niels Bohr Institute, King's College London, TDC NET

What is Kvantify?

Company Mission

"Kvantify develops software that runs both on quantum computers as well as classical compute architectures like CPU and GPU to solve the most challenging problems in chemistry and biology with speed and precision in order to discover molecules that cure diseases."

The Approach

Kvantify bridges current classical high-performance computing with emerging quantum computing to accelerate drug discovery:

1. Classical Computing (Today):

• Optimized physics-based algorithms on CPUs/GPUs

• Provides immediate value to pharma/biotech companies

• Claims 100x faster than comparable methods

2. Quantum-Ready Architecture:

• Algorithms designed to leverage quantum computers as hardware matures

• Can switch between classical and quantum backends

3. Future Quantum Computing:

• FAST-VQE and proprietary quantum algorithms

• Positioned for fault-tolerant quantum computers

CEO Hans Henrik Knudsen: "We view ourselves as problem-solvers, aiming to create ground-breaking tools that deliver value to businesses right now... and are prepared to deliver additional value as quantum computer hardware matures."

Products

Koffee Platform

Computational drug discovery platform for pharmaceutical and biotech companies conducting small molecule drug discovery.

Product 1: Koffee Unbinding Kinetics (March 2024)

Function: Calculates rate at which drug molecule dissociates from target protein

Significance:

• First commercial product for unbinding kinetics calculation

• Traditionally required expensive laboratory experiments (weeks/months)

• Now: Calculations in minutes

• Physics-based (no training data required)

Goal: Reduce time from Hit-ID to lead optimization by 75%

Product 2: Koffee Binding Affinity (October 2024)

Function: Predicts strength of interaction between drug and target protein

Performance Claims:

• 100x faster than comparable state-of-the-art methods

• Same accuracy level

• Calculations in minutes

• No manual setup or training data required

FAST-VQE Algorithm

Proprietary quantum algorithm for quantum chemistry:

• High precision with fewer quantum resources than previous algorithms

• Works on current NISQ quantum devices

• Core intellectual property

Customer/Partner

Tetra Pharm Technologies (Only publicly disclosed customer)

Results:

• Reduced screening time: months to weeks

• Cost efficiency through reduced lab experiments

• SAR-based insights for compound selection

Note: Total customer count not publicly disclosed.

Funding Details

€10 Million Seed Round (July 2024)

Lead Investor: Dreamcraft

• Danish VC, B2B SaaS focus

• Carsten Salling: "The potential of quantum chemical computational drug discovery is massive."

Lundbeckfonden BioCapital

• Danish life science investor

• Jacob Falck Hansen: "Quantum computing can deliver accuracy and derisking to early stages of drug development."

Other Investors:

• 2degrees (private investment)

• Redstone VC (quantum-focused)

• 2xN (Danish quantum VC)

• EIFO

Use of Funds:

• Strengthen quantum computing position

• Accelerate drug discovery solutions

• Develop quantum algorithms

• Strategic partnerships

Leadership Team

Hans Henrik Knudsen - CEO & Co-founder

• Nuclear physicist

• McKinsey & Company (5+ years)

• KMD (Director)

• Advised NATO leadership on quantum technology

Christina Krogsgård Nielsen - Head of Products & Strategy

• Joined September 2023

• Product launches spokesperson

Janus Wesenberg - Head of Research

• Quantum computing research lead

Dr. Jörg Weiser - Chairman (January 2025)

• Molecular design background

• Global business experience

Key Partnerships

Amazon Web Services (AWS)

• AWS Braket platform collaboration

• Multi-vendor quantum hardware access

• AWS Partner status

Niels Bohr Institute

• Developing DanQ - Denmark's quantum computer

• Funded by Novo Nordisk Foundation (1.5 billion DKK)

King's College London

• Neuroscience drug discovery collaboration

TDC NET

• Denmark's largest telecom infrastructure company

• Network optimization application

• "Denmark's largest quantum technology agreement"

European Innovation Council (EIC)

• EU funding recipient

Competitive Landscape

Competitor Comparison Table

Competitor Comparison Table

Company	Founded	Funding	Team Size	Focus Area	Geographic Base	Quantum Approach	Commercial Products	Public Customers
Kvantify	2022	€10M	~60-67	Drug discovery (exclusive)	Denmark/Europe	Hybrid classical-quantum, NISQ-ready	Koffee platform (2 products)	1 disclosed (Tetra Pharm)
Schrödinger	1990	Public (SDGR)	800+	Computational chemistry, materials	USA (NYC)	Exploring quantum, primarily classical	Full drug discovery suite	1,900+ customers (pharma, materials)
ProteinQure	2017	$14M+	~20-30	Protein therapeutics	Canada	Quantum-inspired algorithms	Platform in development	Undisclosed
Qubit Pharmaceuticals	2020	€16M (Series A)	~40-50	Drug discovery	France	Hybrid quantum-classical	Atlas platform	Partnerships disclosed
Menten AI	2018	$4M	~10-20	Protein design	USA	Quantum algorithms for proteins	PSICHIC platform	Limited disclosure
Zapata Computing	2017	$68M+	50+	Multi-industry (including chemistry)	USA	Orquestra quantum platform	Orquestra platform	Multiple industries
QC Ware	2014	$33M+	30+	Multi-industry (~40% finance)	USA	Forge quantum platform	Forge platform	Goldman Sachs disclosed

Key Competitive Insights

Key Competitive Insights

Kvantify vs. Traditional Leaders (Schrödinger):

• Schrödinger Advantages: 30+ years market presence, 1,900+ customers, proven validation, public company resources

• Kvantify Differentiation: Quantum-first approach, physics-based speed claims (100x), exclusive drug discovery focus

• Reality: Schrödinger's established position represents significant competitive barrier

Kvantify vs. Quantum Drug Discovery Peers:

• Funding Position: Middle of pack (€10M vs. $4M-68M range)

• Team Size: Comparable to ProteinQure/Menten, smaller than Qubit/Zapata

• Product Maturity: Has launched commercial products (ahead of some peers)

• Customer Validation: Limited (only 1 public customer vs. undisclosed competitors)

Kvantify vs. Broader Quantum Platforms (Zapata, QC Ware):

• Zapata/QC Ware Advantages: 7-10 year head start, 3-7x more funding, multi-industry diversification, proven enterprise customers

• Kvantify Differentiation: Exclusive life sciences focus, drug discovery domain expertise, European base

• Reality: Broader platforms have significant resource advantages but less specialized drug discovery focus

Competitive Positioning Analysis

Kvantify's Differentiators

1. Exclusive Life Sciences Focus:

• 100% dedicated to drug discovery (vs. multi-industry competitors)

• Deep domain expertise in pharmaceutical applications

• All product development resources concentrated on single vertical

2. Hybrid Classical-Quantum Architecture:

• Delivers value today with classical algorithms (not waiting for quantum)

• Quantum-ready for future hardware improvements

• Hedges against quantum timeline uncertainty

3. Physics-Based Approach:

• Emphasis on interpretability and mechanistic insights

• No requirement for large training datasets

• Regulatory advantage (explainability for FDA/EMA)

4. European Position:

• Strong Danish/European quantum ecosystem

• EU funding support

• Geographic advantage in European pharma market

5. Academic Credibility:

• 35+ PhDs, 7 professors

• Niels Bohr Institute partnership

• FAST-VQE algorithm represents technical contribution

Kvantify's Competitive Disadvantages

1. Funding Gap:

• €10M vs. $33M-68M for quantum competitors

• Limited resources for R&D, sales, marketing

• Shorter runway requiring faster customer acquisition

2. Limited Customer Validation:

• Only 1 public customer (Tetra Pharm)

• Competitors may have more undisclosed customers

• Difficult to prove broad market validation

3. Late Market Entry:

• Founded 2022 vs. competitors from 2014-2020

• 2-8 year disadvantage in market presence

• Behind in customer relationships and brand recognition

4. Geographic Scope:

• Primarily Nordic/European vs. US-based competitors with larger pharma market access

• US expansion planned but not yet established

• Limited global footprint

5. Established Classical Competition:

• Schrödinger has 30+ years validation and 1,900+ customers

• High switching costs from proven methods

• Quantum advantage must be substantial to justify change

Market Position

Target Customers:

• Pharmaceutical companies (small molecule drug discovery)

• Biotech companies (early-stage development)

• Research institutions

Geographic Focus:

• Primary: Denmark/Nordic region

• Secondary: Broader Europe

• Planned: United States expansion

Team Growth:

• 2023: Approximately 50 employees

• 2024: Approximately 67 employees (20% year-over-year growth)

Technology Risks

1. Quantum Advantage Timeline

• Fault-tolerant quantum computers likely 5-10+ years away

• NISQ devices have severe limitations

• Classical computing continues improving rapidly

• Risk: Quantum advantage may not materialize in timeframe needed

2. Scientific Validation

• Computational predictions must translate to lab success

• Pharmaceutical industry requires extensive validation

• Regulatory acceptance uncertain

• Limited public case studies (only Tetra Pharm)

3. Classical Competition

• GPU advances, AI/ML methods improving

• Established methods have decades of validation

• Risk: Classical methods may solve problems adequately

Business Risks

1. Funding Constraints

• €10M seed with 60-67 employees

• Estimated burn rate: €500K-1M/month

• Likely 10-20 month runway

• Series A needed 2025-2026

2. Customer Acquisition

• Only one public customer (Tetra Pharm)

• Pharma sales cycles: 12-24 months

• Conservative industry, high switching costs

• Market education burden

3. Competitive Pressure

• Better-funded quantum competitors

• Established classical chemistry companies

• Tech giants (Google, IBM, Microsoft) entering space

Realistic Assessment

What Has Been Achieved

1. €10M seed from credible investors

2. 60-67 employees (35+ PhDs, 7 professors)

3. Two commercial products launched

4. AWS partnership established

5. At least one commercial customer

6. Three office locations

7. Academic partnerships

8. EU funding secured

What Remains Unproven

1. Broad customer adoption

2. Revenue scale (not disclosed)

3. Scientific validation across multiple programs

4. Clear quantum advantage in production

5. Path to profitability

6. US market presence

Outlook and Scenarios

Most Likely Outcome (50% probability)

Acquisition 2027-2029 at €30-100M

Likely Acquirers:

• Pharmaceutical companies (Roche, Novo Nordisk, AstraZeneca)

• Computational chemistry companies (Schrödinger, etc.)

• Quantum computing companies (Zapata, QC Ware)

• Cloud providers (AWS, Azure)

Valuation Drivers:

• Customer traction (number and quality)

• Scientific validation (publications, case studies)

• Technology differentiation demonstrated

• Team quality and retention

Bull Case (30% probability)

Scenario:

• Quantum hardware improves faster than expected (2026-2028)

• Demonstrates clear quantum advantage

• Builds 10-20+ pharma/biotech customers

• Series A and B successful ($20-50M additional)

• Acquisition $100-300M or independent growth toward IPO

Bear Case (20% probability)

Scenario:

• Quantum advantage delayed beyond 2030

• Classical AI/ML proves equally effective

• Difficulty building customer base

• Challenging Series A fundraising

• Down-round or acquisition $10-30M

Key Success Factors

For Kvantify to Succeed:

1. Demonstrate Quantum Advantage: Clear cases where quantum outperforms classical

2. Build Customer Base: 10+ pharma/biotech customers by 2026-2027

3. Scientific Validation: Predictions translate to lab success

4. Series A Funding: $15-30M by 2025-2026

5. Product Development: Expand Koffee capabilities

6. Team Retention: Keep founders and key PhDs

Final Assessment

Kvantify is a scientifically credible early-stage quantum drug discovery company with:

Strengths:

• Strong technical team (35+ PhDs, 7 professors)

• €10M from credible investors (Dreamcraft, Lundbeckfonden, Redstone)

• Real products launched (Koffee platform - not vaporware)

• Strategic partnerships (AWS, Niels Bohr Institute, King's College London)

• Clear life sciences focus (100% drug discovery)

• European quantum ecosystem advantages

Critical Uncertainties:

• Quantum advantage timeline (5-10+ years uncertain)

• Customer adoption (only 1 public customer: Tetra Pharm)

• Revenue and profitability path unknown

• Classical computing competition intense

• Long pharmaceutical validation requirements

• Funding gap vs. competitors (€10M vs. $33-68M)

Most Probable Outcome: Acquisition by pharma company, computational chemistry firm, or larger quantum player in 2027-2029 at €30-100M, with value dependent on customer traction and technology validation achieved.

Bottom Line: Impressive technical capabilities and meaningful funding secured, but faces classic deep tech challenges: long validation timelines, uncertain technology advantages, conservative target industry. Success depends on quantum hardware maturation timeline, customer adoption rate, and demonstrating clear advantages over established classical methods from companies like Schrödinger with 30+ years of market validation.