Citadel Discovery

The Promise of AI/ML for Drug Discovery

The costs of drug discovery continue to rise, with estimates exceeding $2 Billion and 10-12
years in research and development. AI/ML in Drug Discovery holds a key to the promise of
reducing these costs and timelines

The Challenge

Data Scarcity Slows the Adoption of AI in Small Molecule Drug Discovery and Novel Biology Exploration due to

Costly data generation
Complexities of data sharing
Slow and expensive ML model validation cycles

Because of this data scarcity, exploration of novel biology lags behind technological capability

Pharma: High-risk of pursuing novel protein targets is not justified without target validation
Academia: High cost of data prevents ID of tool molecules to validate targets
Result: redundancy of proteins targets pursued
Missed opportunity to impact disease in 90+% of under-explored proteome

Citadel Discovery was launched in 2021 with the purpose of democratizing access to the data and technology that will drive the future of biological exploration, drug discovery and health technologies.

Citadel Drives Disruption

Democratizing Predictive Drug Discovery

Problem
Data Scarcity

Disruption:

Low-cost access to data & data generating technologies (e.g. DEL)

Invest in novel technologies for cost-accessible data generation

Problem
Slow AI-Model Validation Cycle

Disruption:

Build an industry enabling rapid chemical synthesis and testing capability for model validation

Enable drug discovery model validation at pace of high-tech

Problem
Data Sharing

Disruption:

Build data sharing & commercialization platform

Generate proteome-wide data set for drug discovery, diagnostics and precision medicine

Problem
Underfunding of Industry Enabling Capabilities

Disruption:

Enable new and Underutilized technologies to drive down costs

Align funding sources with public benefit