SciFinder-n
E204176
SciFinder-n is a comprehensive research discovery platform from Chemical Abstracts Service that enables scientists to search and analyze chemical substances, reactions, literature, and related scientific data.
All labels observed (7)
| Label | Occurrences |
|---|---|
| CAS SciFinder-n | 3 |
| SciFinder | 3 |
| CAS SciFinder Discovery Platform | 1 |
| CAS SciFinder-n platform | 1 |
| SciFinder database | 1 |
| SciFinder-n canonical | 1 |
| SciFinderⁿ | 1 |
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
chemical information database
ⓘ
research discovery platform ⓘ scientific literature search tool ⓘ |
| accessMode |
subscription-based
ⓘ
web-based interface ⓘ |
| developer | Chemical Abstracts Service ⓘ |
| field |
chemical engineering
ⓘ
chemistry ⓘ life sciences ⓘ materials science ⓘ pharmaceutical sciences ⓘ |
| hasFeature |
access to CAS REACT
ⓘ
access to CAS REGISTRY ⓘ access to journal literature ⓘ access to patent data ⓘ biosequence searching ⓘ citation analysis ⓘ filtering and refinement tools ⓘ patent landscape analysis ⓘ predictive retrosynthesis algorithms ⓘ property data retrieval ⓘ reaction condition analysis ⓘ reaction search ⓘ reference searching ⓘ retrosynthesis planning ⓘ structure search ⓘ substructure search ⓘ supplier information for chemicals ⓘ visualization of results ⓘ |
| language | English ⓘ |
| mainSubject |
chemical reactions
ⓘ
chemical substances ⓘ patents ⓘ scientific data ⓘ scientific literature ⓘ |
| operatingSystem | platform-independent (web browser) ⓘ |
| parentOrganization | American Chemical Society ⓘ |
| predecessor |
SciFinder-n
self-linksurface differs
ⓘ
surface form:
SciFinder
|
| publisher | Chemical Abstracts Service ⓘ |
| targetAudience |
academic researchers
ⓘ
industrial scientists ⓘ information professionals ⓘ |
| useCase |
chemical structure searching
ⓘ
competitive intelligence in chemistry ⓘ drug discovery research ⓘ literature review ⓘ patent searching ⓘ reaction route planning ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: SciFinder-n Description of subject: SciFinder-n is a comprehensive research discovery platform from Chemical Abstracts Service that enables scientists to search and analyze chemical substances, reactions, literature, and related scientific data.
Referenced by (11)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
CAS SciFinder Discovery Platform
this entity surface form:
SciFinder
this entity surface form:
SciFinder
this entity surface form:
SciFinderⁿ
subject surface form:
CAS Registry Number
this entity surface form:
SciFinder database
subject surface form:
CAS Registry Number
this entity surface form:
CAS SciFinder-n platform
this entity surface form:
CAS SciFinder-n
this entity surface form:
CAS SciFinder-n
this entity surface form:
SciFinder
this entity surface form:
CAS SciFinder-n