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Spring Science

Spring Science

Therapeutic Areas

With societal-wide advances in areas including artificial intelligence and computational technology, we can now run more complex, industrialized drug discovery R&D, but we’re missing the tooling to make sense of the outcomes of these experiments. Despite these advances, scientists still need to rely on yesterday’s tools to solve today’s problems. High content imaging (HCI) is an exceptionally data-rich tool used in many pipelines that is growing both in use and data scale, but massive technological improvements in computing and image analysis in adjacent fields have not transferred to HCI, meaning scientists involved in HCI have been left behind. In most cases, users are forced to utilize antiquated, disjointed software packages to make decisions about their assays, resulting in lengthened decision cycles and a lack of confidence in their data quality and results. Scientists utilizing both existing commercial and internal tools often have to become masters at more than 4 different software interfaces just to determine if their assay worked, and to make decisions on the results of the screens. Additionally, many times being able to visualize the data means that scientists spend significant amounts of time writing custom code and perfecting visualizations.

The Spring Engine allows scientists to easily move from raw images, through quality control to feature extraction and data visualization, resulting in a process where scientists can combine the best of human knowledge and intuition with the power of unbiased, AI-powered image analysis and clustering in the web browser.

AI-Enabled Services:

  • Research services that use machine learning (ML) algorithms

  • Platform for image analysis using deep learning-powered AI embeddings

Utilize Spring Science AI technologies in the following spaces:

  • Biology, including target discovery, imaging and HTS

  • Data, including data transfer, analysis and storage

  • Drug Metabolism, including pharmacokinetics and ADME

  • Pharmacology, including in vitro and in vivo disease models

  • Safety, including cardiovascular, pulmonary, neuro and reproductive toxicology

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