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Engine Biosciences

Engine Biosciences

San Francisco, United States· Est.
enginebio.com
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Private Company

Total funding raised: $53M

Overview

Engine Biosciences is a private, pre-clinical stage biotech founded in 2015, leveraging a proprietary platform combining machine learning algorithms and large-scale combinatorial CRISPR screening to map disease biology and discover novel drug targets and biomarkers. The company is advancing a pipeline focused on targeted therapies for solid tumors and collaborates with partners in other therapeutic areas. Backed by a seasoned leadership team and prominent investors, Engine aims to translate deep biological insights into impactful precision medicines.

Oncology

Technology Platform

Integrated platform combining proprietary combinatorial CRISPR screening and machine learning algorithms to map genetic interaction networks, identify novel drug targets, biomarkers, and therapeutic combinations.

Funding History

2
Total raised:$53M
Series A$43M
Seed$10M

Opportunities

The growing precision medicine market, especially in oncology, offers a significant opportunity for therapies guided by robust biomarkers.
The platform's ability to deconvolve biological complexity and identify novel targets and combinations can address high unmet needs in treatment-resistant cancers.
Strategic partnerships can provide non-dilutive funding and expand the platform's application into new therapeutic areas.

Risk Factors

Key risks include the unproven translational success of its AI/ML platform in human clinical outcomes, intense competition in the computational drug discovery space, and operational complexity from its dual-continent structure.
As a pre-revenue company, it remains dependent on investor funding and milestone achievement.

Competitive Landscape

Engine operates in the competitive AI-driven drug discovery space, competing with firms like Recursion, Exscientia, Insilico Medicine, and Relay Therapeutics. Its differentiation lies in the tight integration of large-scale combinatorial CRISPR data generation with proprietary machine learning models. The focus on genetic interaction networks and synthetic lethality in oncology places it against both large pharma oncology divisions and specialized biotechs.

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