Build your knowledge graph around what matters most: the scientific entity

Most systems treat entities — molecules, proteins, cell lines, assays — as metadata. Chemantics makes them the core. Connect your chemistry and biology data around the entities that drive discovery, so every experiment, result, and decision traces back to what you're actually studying.

From fragmented data to entity-centered knowledge graphs

Build your knowledge graph around what matters most: the scientific entity

Most systems treat entities — molecules, proteins, cell lines, assays — as metadata. Chemantics makes them the core. Connect your chemistry and biology data around the entities that drive discovery, so every experiment, result, and decision traces back to what you're actually studying.

From fragmented data to entity-centered knowledge graphs

Build your knowledge graph around what matters most: the scientific entity

Most systems treat entities — molecules, proteins, cell lines, assays — as metadata. Chemantics makes them the core. Connect your chemistry and biology data around the entities that drive discovery, so every experiment, result, and decision traces back to what you're actually studying.

From fragmented data to entity-centered knowledge graphs

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Partners

Trusted by leading organizations in scientific research and innovation.

Partners

Trusted by leading organizations in scientific research and innovation.

Partners

Trusted by leading organizations in scientific research and innovation.

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Why Chemantics?

Traditional systems store scientific data in tables and documents, then try to reconstruct relationships later.

Chemantics makes scientific relationships explicit from day one. Instead of relying on teams to reconstruct links after the fact, Chemantics captures relationships, context, provenance, and lineage directly in the data model — and automatically extends that semantic structure across integrated datasets as well.

The result is a persistent scientific knowledge layer that supports traceable decisions, explainable reasoning, and AI-ready discovery.

Register compounds, biologics, formulations, materials, batches, and samples as connected graph entities.

Automatically derive and preserve relationships, context, provenance, and lineage.

Ground LLMs and AI workflows in trusted scientific knowledge.

Turn disconnected R&D data into reusable scientific knowledge for search, reasoning, and AI workflows.

Why Chemantics?

Traditional systems store scientific data in tables and documents, then try to reconstruct relationships later.

Chemantics makes scientific relationships explicit from day one. Instead of relying on teams to reconstruct links after the fact, Chemantics captures relationships, context, provenance, and lineage directly in the data model — and automatically extends that semantic structure across integrated datasets as well.

The result is a persistent scientific knowledge layer that supports traceable decisions, explainable reasoning, and AI-ready discovery.

Register compounds, biologics, formulations, materials, batches, and samples as connected graph entities.

Automatically derive and preserve relationships, context, provenance, and lineage.

Ground LLMs and AI workflows in trusted scientific knowledge.

Turn disconnected R&D data into reusable scientific knowledge for search, reasoning, and AI workflows.

Why Chemantics?

Traditional systems store scientific data in tables and documents, then try to reconstruct relationships later.

Chemantics makes scientific relationships explicit from day one. Instead of relying on teams to reconstruct links after the fact, Chemantics captures relationships, context, provenance, and lineage directly in the data model — and automatically extends that semantic structure across integrated datasets as well.

The result is a persistent scientific knowledge layer that supports traceable decisions, explainable reasoning, and AI-ready discovery.

Register compounds, biologics, formulations, materials, batches, and samples as connected graph entities.

Automatically derive and preserve relationships, context, provenance, and lineage.

Ground LLMs and AI workflows in trusted scientific knowledge.

Turn disconnected R&D data into reusable scientific knowledge for search, reasoning, and AI workflows.

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Connect compounds, assays, biomarkers, diseases, and publications into a queryable knowledge graph.

Connected scientific knowledge enables a structured workflow that transforms fragmented data into traceable intelligence.

1. Unify

Integrate diverse data from internal systems and external sources into a single foundation for connected scientific knowledge.

2. Connect

Connect scientific entities, relationships, and context into a domain-aware scientific knowledge graph.

3. Reason

Apply scientific knowledge and AI to derive transparent, explainable insights from connected data.

4. Decide

Support evidence-based decisions with complete scientific context and traceability.

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Semantic Research Dossier

A Semantic Data Platform for Scientific Entities

SRD is a semantic platform for scientific data integration — built around the entities that drive research, development, quality, and compliance.

It connects compounds, materials, formulations, batches, samples, experiments, and processes into a knowledge graph, preserving the relationships and lineage teams need for traceable decisions and trustworthy AI.

emantic

Connect data through meaning, relationships, and context.

Entities
Relationships
Context
esearch

Model scientific entities as first-class data objects.

Compounds
Batches
Samples
Experiments
ossier

Organize evidence into a traceable decision foundation.

Evidence
Lineage
Decisions

See how it works

Semantic Research Dossier

A Semantic Data Platform for Scientific Entities

SRD is a semantic platform for scientific data integration — built around the entities that drive research, development, quality, and compliance.

It connects compounds, materials, formulations, batches, samples, experiments, and processes into a knowledge graph, preserving the relationships and lineage teams need for traceable decisions and trustworthy AI.

emantic

Connect data through meaning, relationships, and context.

Entities
Relationships
Context
esearch

Model scientific entities as first-class data objects.

Compounds
Batches
Samples
Experiments
ossier

Organize evidence into a traceable decision foundation.

Evidence
Lineage
Decisions

See how it works

Semantic Research Dossier

A Semantic Data Platform for Scientific Entities

SRD is a semantic platform for scientific data integration — built around the entities that drive research, development, quality, and compliance.

It connects compounds, materials, formulations, batches, samples, experiments, and processes into a knowledge graph, preserving the relationships and lineage teams need for traceable decisions and trustworthy AI.

emantic

Connect data through meaning, relationships, and context.

Entities
Relationships
Context
esearch

Model scientific entities as first-class data objects.

Compounds
Batches
Samples
Experiments
ossier

Organize evidence into a traceable decision foundation.

Evidence
Lineage
Decisions

See how it works

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Scientific Knowledge in Practice

Trace every relationship back to evidence, source data, and scientific context.

Register compounds, biologics, formulations, materials, and samples as graph-native entities. Automatically derive relationships, context, and lineage to create connected scientific knowledge.

Register

Register compounds, biologics, ingredients, materials, formulations, and samples as graph-native entities.

Connect

Automatically derive scientific relationships from structures, compositions, formulations, reactions, and experiments.

Trace

Trace origins, transformations, and dependencies across compounds, materials, formulations, experiments, and decisions.

Example: Register a small protein using a HELM Editor.

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Connected Scientific Intelligence

Where connected scientific knowledge accelerates R&D

See how connected scientific data accelerates decisions across regulated and innovation-driven industries.

Healthcare and Consumer Products

Fragmented scientific and regulatory data slows decisions across the product lifecycle.

SRD connects and contextualizes data to accelerate decisions, reduce compliance risk, and improve product quality.

Pharmaceuticals
Pharmaceuticals
Pharmaceuticals
Cosmetics
Cosmetics
Cosmetics
Nutraceuticals
Nutraceuticals
Nutraceuticals
Chemicals & Materials Innovation

Siloed structure, formulation, and process data makes performance hard to predict and optimize.

Connect experimental, simulation, and production data to accelerate development and improve product performance.

Chemicals
Chemicals
Chemicals
Advanced Materials
Advanced Materials
Advanced Materials

Explore how SRD connects scientific data across regulated, formulation-driven, and materials-focused workflows.

See how SRD works

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From Idea to Impact

Example Use Cases

Real-world scenarios that turn concepts into measurable outcomes

Scientific Registries and Semantic Harmonization

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Science is complex.

Your decisions don't have to be.

See how Chemantics helps your team turn scientific data into decisions—in days, not months.

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