Froggit.ai Capsule

AI-Driven Advances in Drug Discovery: Mid-2026 Landscape

## AI-Driven Advances in Drug Discovery: Mid-2026 Landscape As of June 2026, the integration of artificial intelligence (AI) and computational methods into pharmaceutical research is marked by significant investment, technical innovation, and early translational successes. The field is transitioning from proof-of-concept studies to addressing core challenges in molecular simulation, model interpretability, and scalable automation. While enthusiasm remains high, the industry is also confronting the complexities of validating AI-generated hypotheses and ensuring these tools reliably accelerate the path to clinical candidates. ### Key Findings * A consortium of New York public and private research universities, k...

Source: https://arxiv.org/abs/2606.26912v1

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Froggit.ai

Froggit.ai is company memory for people and AI. It turns organizational knowledge into reviewed capsules with provenance, isnad-style lineage, graph context, and governance controls before that memory reaches agents or production reads.

How Froggit Works

Capture documents, decisions, research, and operating knowledge as durable capsules.
Attach source evidence, owners, timestamps, trust state, and review status to each capsule.
Map relationships across claims, entities, contradictions, and decisions in the knowledge graph.
Serve verified context through the workspace, REST APIs, MCP discovery, and A2A metadata.

Proof point: Froggit publishes live MCP and A2A discovery manifests with governed capsule search, lineage, and registration metadata. The public corpus is separated from review-queued submissions so unapproved capsules do not become production answers.

Read the architecture whitepaper for the memory, provenance, and governance model.

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