Coincidence Labs AI Platform

Coincidence Labs AI Platform is an AI copilot designed specifically for pharmaceutical R&D teams, including pharma organisations, CROs, IP advisors, and strategy consultants. The platform deploys purpose-built AI agents that automate the ingestion and structuring of data from patents, scientific literature, clinical trials, and internal sources — enabling experts to spend less time buried in documents and more time generating value. By turning fragmented R&D data into defensible, traceable insights, Coincidence Labs helps biopharma teams test hypotheses faster, uncover opportunities, and align decisions across functions.

From Target Product Profile (TPP) definition through to batch liability detection and IND-enabling study prioritisation, the platform is built to address the full spectrum of research-heavy challenges that slow drug development programmes. Its intelligence layer connects chemists, DMPK experts, toxicologists, and programme leaders around a shared, evidence-backed foundation for decision-making.

Core Platform Capabilities

• Automated research workflows: AI agents automate ingestion and structuring of data from patents, literature, clinical trials, chemistry databases, toxicity databases, and internal on-premise sources, dramatically reducing manual review time.
• Patent intelligence at scale: Unstructured patents are transformed into actionable intelligence. The platform surfaces novel compounds, competitive signals, and freedom-to-operate (FTO) risks — cutting weeks of costly patent analysis into minutes and reducing errors that could waste months of R&D effort.
• Markush claim expansion and FTO mapping: AI agents can expand Markush claims, screen structural overlap against patent families, and map the FTO landscape for scaffolds, R-groups, formulations, and method-of-use across lead series.
• Molecular data interaction: Teams can interact with molecular data in a traceable and collaborative environment, with properties including aqueous solubility, PAMPA permeability, Caco-2 permeability, blood-brain barrier penetration, plasma protein binding, hepatic clearance, and hERG toxicity surfaced directly within workflows.
• AI-powered Target Product Profile (TPP) builder: A guided, multi-step tool that helps teams define disease area, indication, and patient population to structure programme strategy from the outset.
• Molecule extraction and analysis: The platform can extract new molecules from documents and data sources, enabling teams to import compounds into projects and immediately analyse efficacy optimisation strategies and toxicity issues.
• Agentic search across data sources: A unified agentic search capability spans scientific literature, clinical data, on-premise data, chemistry databases, toxicity databases, documents, and tool integrations to surface defensible evidence for programme prioritisation decisions.
• Institutional memory: Insights generated from individual programme analyses are retained and made accessible across the organisation, ensuring knowledge compounds over time and benefits the entire portfolio.

Benefits by User Group

• Pharma teams: Accelerate progress from target to clinic by compressing patent and literature review timelines, keeping programmes ahead of competitors and on track to key milestones.
• CROs: Surface known liabilities and competitive overlaps early to reduce the risk of dead-end investments and late-stage surprises.
• IP advisors: Conduct large-scale FTO analysis and competitive patent landscaping with greater speed and accuracy.
• Strategy consultants and portfolio leaders: Make stage-gate and portfolio allocation decisions with traceable, evidence-backed insights that leadership and governance boards can trust, and identify risks and opportunities earlier to maximise overall pipeline value.

Security, Privacy, and Compliance

• Enterprise-grade security: Sensitive data is protected with encryption in transit and at rest, access controls, and full auditability built into the platform.
• Data privacy: Customer data remains fully owned by the customer. Internal documents and experimental data are never used to train models or shared outside the customer's environment.
• Compliance certifications: The platform is aligned with SOC 2, GDPR, and ISO standards, with authenticated and encrypted data handling throughout.
• Dedicated support: A team of drug discovery experts and machine learning scientists provides dedicated onboarding and responsive ongoing support.