Spring Discovery, Inc.
Spring Discovery, now rebranded as Spring Science, is a leader in applying artificial intelligence and machine learning to biological research and drug discovery. The company develops advanced computational tools, including high-content image analysis, phenotypic profiling, and cellular analysis, to accelerate scientific breakthroughs and support the broader scientific community. Their platform, the Spring Engine, enables complex data analysis, phenotypic screening, and discovery of novel therapeutic targets across various biological pathways. Spring Science is committed to fostering innovation in immunology, aging, inflammasome biology, cellular profiling, and vaccine development, leveraging AI to uncover new insights and facilitate the development of life-changing therapies.
Industries
Nr. of Employees
small (1-50)
Spring Discovery, Inc.
San Carlos, California, United States, North America
Products
Annotated phenotypic benchmark datasets
Curated phenotypic datasets and benchmarking plates for algorithm validation and compound-similarity benchmarking, generated from multi-plate profiling experiments.
Annotated phenotypic benchmark datasets
Curated phenotypic datasets and benchmarking plates for algorithm validation and compound-similarity benchmarking, generated from multi-plate profiling experiments.
Services
Cloud-based ML image-analysis platform for high-content imaging
A cloud-deployable software platform that provides supervised and unsupervised modeling, single-cell clustering, embeddings, and a GUI enabling non-experts to train and apply ML models to microscopy datasets.
High-content phenotypic screening and assay development
Design and execution of high-throughput, image-based screens (including PBMC-based immune assays, inflammasome activation assays and Cell Painting) with downstream ML analysis and hit prioritization.
Benchmarking and compound-similarity analysis
Generation of benchmark datasets and application of phenotypic-similarity algorithms to validate compound signals, assess reproducibility across donors/plates, and create similarity classes for mechanism inference.
Preclinical in vivo validation studies
Preclinical animal model experiments to validate in vitro/ML-derived hypotheses and therapeutic effects prior to advancement into development programs.
Cloud-based ML image-analysis platform for high-content imaging
A cloud-deployable software platform that provides supervised and unsupervised modeling, single-cell clustering, embeddings, and a GUI enabling non-experts to train and apply ML models to microscopy datasets.
High-content phenotypic screening and assay development
Design and execution of high-throughput, image-based screens (including PBMC-based immune assays, inflammasome activation assays and Cell Painting) with downstream ML analysis and hit prioritization.
Benchmarking and compound-similarity analysis
Generation of benchmark datasets and application of phenotypic-similarity algorithms to validate compound signals, assess reproducibility across donors/plates, and create similarity classes for mechanism inference.
Preclinical in vivo validation studies
Preclinical animal model experiments to validate in vitro/ML-derived hypotheses and therapeutic effects prior to advancement into development programs.
Expertise Areas
- High-content phenotypic screening
- Machine learning for biological image analysis
- Single-cell analysis and clustering
- Phenotypic drug discovery and hit triage
Key Technologies
- High-content microscopy
- Cell Painting morphological staining
- Live-cell fluorescent and label-free brightfield imaging
- Convolutional neural networks (CNNs) and transfer learning