Insitro
insitro is a pioneering drug company leveraging machine learning and large-scale data to decode the complexities of biology and accelerate the development of transformative medicines. Their mission is to bring better drugs faster to patients in need, with a vision driven by the convergence of human biology and machine learning. The company fosters a diverse, collaborative culture of scientists, engineers, and drug hunters working together to generate scientific breakthroughs and redefine drug discovery and development.
Industries
Nr. of Employees
medium (51-250)
Products
ML-driven Drug Discovery Platform
A platform integrating in vitro cellular data with clinical data to accelerate new medicine development using machine learning.
Therapeutic Program Development
Development of therapeutic programs in metabolism, oncology, and neuroscience, leveraging insights from the ML-driven platform.
Machine Learning for Disease Redefinition
Use of machine learning and AI to build models for disease state interpretation and therapeutic intervention identification.
Data Aggregation for Drug Discovery
Collection and aggregation of large-scale clinical and phenotypic data to fuel machine learning models for drug discovery.
Automated Laboratory Technologies
Utilization of automated laboratories to generate multi-modal phenotypic cellular data for machine learning analysis.
Pipeline Development
Creation of a pipeline for in-house and partnered drug discovery programs across multiple therapeutic areas.
ML-driven Drug Discovery Platform
A platform integrating in vitro cellular data with clinical data to accelerate new medicine development using machine learning.
Therapeutic Program Development
Development of therapeutic programs in metabolism, oncology, and neuroscience, leveraging insights from the ML-driven platform.
Machine Learning for Disease Redefinition
Use of machine learning and AI to build models for disease state interpretation and therapeutic intervention identification.
Data Aggregation for Drug Discovery
Collection and aggregation of large-scale clinical and phenotypic data to fuel machine learning models for drug discovery.
Automated Laboratory Technologies
Utilization of automated laboratories to generate multi-modal phenotypic cellular data for machine learning analysis.
Pipeline Development
Creation of a pipeline for in-house and partnered drug discovery programs across multiple therapeutic areas.