Insitro
E298582
Insitro is a biotechnology company that uses machine learning and high-throughput biology to accelerate drug discovery and development.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Insitro canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T2790852 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Insitro Context triple: [Daphne Koller, coFounded, Insitro]
-
A.
Regeneron Pharmaceuticals
Regeneron Pharmaceuticals is a leading American biotechnology company known for developing innovative antibody-based therapies for serious diseases, including eye disorders, cancer, and inflammatory conditions.
-
B.
Verily Life Sciences LLC
Verily Life Sciences LLC is a health-focused research and technology company that develops tools and platforms to collect, organize, and analyze health data, particularly in areas like clinical research, precision health, and disease management.
-
C.
BioNTech
BioNTech is a German biotechnology company best known for developing one of the first mRNA-based COVID-19 vaccines in partnership with Pfizer.
-
D.
Alnylam Pharmaceuticals
Alnylam Pharmaceuticals is a biopharmaceutical company specializing in the development of RNA interference (RNAi)-based therapeutics for genetically defined diseases.
-
E.
Genmab
Genmab is a Danish biotechnology company specializing in the development of antibody-based cancer therapies.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Insitro Target entity description: Insitro is a biotechnology company that uses machine learning and high-throughput biology to accelerate drug discovery and development.
-
A.
Regeneron Pharmaceuticals
Regeneron Pharmaceuticals is a leading American biotechnology company known for developing innovative antibody-based therapies for serious diseases, including eye disorders, cancer, and inflammatory conditions.
-
B.
Verily Life Sciences LLC
Verily Life Sciences LLC is a health-focused research and technology company that develops tools and platforms to collect, organize, and analyze health data, particularly in areas like clinical research, precision health, and disease management.
-
C.
BioNTech
BioNTech is a German biotechnology company best known for developing one of the first mRNA-based COVID-19 vaccines in partnership with Pfizer.
-
D.
Alnylam Pharmaceuticals
Alnylam Pharmaceuticals is a biopharmaceutical company specializing in the development of RNA interference (RNAi)-based therapeutics for genetically defined diseases.
-
E.
Genmab
Genmab is a Danish biotechnology company specializing in the development of antibody-based cancer therapies.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
biotechnology company
ⓘ
private company ⓘ |
| aimsTo |
accelerate drug discovery
ⓘ
improve probability of success in clinical development ⓘ leverage human genetics for target validation ⓘ reduce cost of drug development ⓘ |
| approach |
building predictive models from large-scale biological datasets
ⓘ
integration of machine learning with high-throughput biological experiments ⓘ |
| businessModel |
R&D collaborations with pharma partners
ⓘ
internal drug discovery programs ⓘ |
| collaboratesWith | major pharmaceutical companies ⓘ |
| country |
United States
ⓘ
linked to:
United States of America
|
| employs |
biologists
ⓘ
computational biologists ⓘ machine learning scientists ⓘ software engineers ⓘ |
| focusesOn |
drug development
ⓘ
drug discovery ⓘ |
| foundedBy | Daphne Koller ⓘ |
| foundedInYear | 2018 ⓘ |
| hasCompanyType | venture-backed startup ⓘ |
| hasKeyPerson | Daphne Koller ⓘ |
| hasResearchArea |
cellular models of disease
ⓘ
computational biology ⓘ functional genomics ⓘ genetics ⓘ machine learning for biology ⓘ |
| headquartersLocation |
South San Francisco, California, United States
ⓘ
linked to:
South San Francisco
|
| inception | 2018 ⓘ |
| industry |
biotechnology
ⓘ
pharmaceuticals ⓘ |
| keyRole | Daphne Koller, CEO ⓘ |
| languageOfWork | English ⓘ |
| operatesInSector |
healthcare technology
ⓘ
life sciences ⓘ |
| specializesIn |
data-driven biology
ⓘ
drug candidate optimization ⓘ machine-learning-driven drug discovery ⓘ target discovery ⓘ |
| usesTechnology |
automation
ⓘ
data science ⓘ high-throughput biology ⓘ machine learning ⓘ |
| website | https://www.insitro.com/ ⓘ |
| worksOn |
biologic therapeutics
ⓘ
small-molecule therapeutics ⓘ |
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: Insitro Description of subject: Insitro is a biotechnology company that uses machine learning and high-throughput biology to accelerate drug discovery and development.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.