The startup has developed a clinical research platform that utilizes machine learning to target the biological processes of aging, facilitating the discovery of therapies for age-related diseases such as cardiovascular and neurodegenerative conditions. By enabling pharmaceutical companies to enhance medical treatments, the platform addresses the need for more effective interventions in aging-related health issues.
Funding
$48M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Drug discovery for age-related diseases is hampered by the increasing size and complexity of scientific data, while researchers often lack access to advanced computational tools needed to analyze high-content image data effectively. This limits the ability to identify potential therapeutic targets and develop effective interventions for aging-related health issues.
Solution
Spring Science provides an AI-powered platform designed to empower scientists with advanced tools for high-content image analysis, accelerating the discovery of therapies for age-related diseases. The platform enables researchers to harness the power of machine learning to analyze complex biological processes and identify potential drug candidates. By combining human expertise with artificial intelligence, Spring Science streamlines workflows, amplifies research impact, and fosters collaboration between wet lab and dry lab environments. The platform's AI-driven models can predict adjuvant immune activation and decipher subtle immune activation characteristics of unknown adjuvants of interest.
Target Audience
The primary target audience includes pharmaceutical companies, biotech firms, academic research groups, and non-profits involved in drug discovery and research related to aging and age-related diseases.
Features
- AI-powered high-content image analysis suite for identifying complex phenotypic signatures.
- Machine learning models for predicting compound hits and therapeutic targets.
- Tools for building AI-powered in vitro models of biological processes.
- Cloud-based platform accessible to both industry and academic researchers.
- Capabilities for analyzing single-cell phenotypes and well-level imaging data.
- Integration of data from primary human PBMCs for building aging models.