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South San Francisco, CA, United States of America

Insitro, Inc.

USPTO Granted Patents = 26

EPO Patents = 2

Trademarks = 3

Forward Citations = 43

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44 inventors (with patents filed for the assignee):

Jeevaa Velayutham (12 out of 12 patents)

Hervé Marie-Nelly (9 out of 9 patents)

Christopher Probert (6 out of 6 patents)

4. Zachary Phillips (5 out of 5 patents)

5. Shengjiang Tu (5 out of 6 patents)

6. Francesco Paolo Casale (4 out of 4 patents)

7. Daphne Koller (3 out of 8 patents)

8. Haoyang Zeng (3 out of 3 patents)

9. Michael Bereket (3 out of 3 patents)

10. Matthew Albert (3 out of 3 patents)

11. Max R Salick (3 out of 3 patents)

12. Ci Chu (2 out of 6 patents)

13. Cynthia Hao (2 out of 2 patents)

14. Adelaide Woicik (2 out of 2 patents)

15. Zachary R McCaw (2 out of 2 patents)

Core Competencies:

RNA Sequencing

DNA-Encoded Libraries

Combinatorial Synthesis

Machine Learning

Biological Imaging

Spatial Omics Data

Tissue Microarray

Artifact Detection

Predictive Biomarkers

Cellular Imaging

Disease Modeling

Image Transformation

26 patents: Sort by Forward Citations

1. 12755567 - Machine-learning-enabled imputation of spatial omics data based on histopathology image data (2026-10-06),

2. 12668841 - In situ sequencing of RNA transcripts with non-uniform 5' ends (2026-06-30),

3. 12665048 - Synthon embeddings for modeling DNA-encoded libraries (2026-06-23),

4. 12655420 - Oligonucleotide directed and recorded combinitorial synthesis of encoded probe molecules (2026-06-16),

5. 12541983 - Biological image transformation using machine-learning models (2026-02-03),

6. 12511918 - Biological image transformation using machine-learning models (2025-12-30),

7. 12505549 - Microscopy image analyses for disease modeling (2025-12-23),

8. 12482280 - Biological image transformation using machine-learning models (2025-11-25),

9. 12462386 - Autonomous cell imaging and modeling system (2025-11-04),

10. 12450926 - Machine-learning-enabled imputation of spatial omics data based on histopathology image data (2025-10-21),

11. 12333725 - Systems and methods for determining tissue microarray sampling protocols (2025-06-17),

12. 12332970 - Biological image transformation using machine-learning models (2025-06-17),

13. 12299884 - Machine-learning-enabled predictive biomarker discovery and patient stratification using standard-of-care data (2025-05-13),

14. 12299856 - Systems and methods for artifact detection and removal from image data (2025-05-13),

15. 12277711 - Cellular time-series imaging, modeling, and analysis system (2025-04-14),

Company Overview

Insitro, Inc. is a biotechnology company based in South San Francisco, California, that leverages machine learning and high-throughput biology to transform drug discovery and development. The organization specializes in creating predictive models of disease and drug response, aiming to accelerate the identification of novel therapeutics. By integrating advanced computational techniques with biological insights, Insitro seeks to enhance the efficiency of the pharmaceutical pipeline and improve patient outcomes.

Profile summary synthesized from public USPTO records and patent filings.

Company Badge

USPTO Data Jan 1 1976 - Oct 13 2026

26 Patents

#50 in South San Francisco, CA

#3,041 in California

#12,859 in US

#24,806 in the World

South San Francisco, CA, United States of America

Insitro, Inc.

Forward Citations = 43

44 inventors:

Jeevaa Velayutham (12 out of 12 patents)

Hervé Marie-Nelly (9 out of 9 patents)

Christopher Probert (6 out of 6 patents)

4. Zachary Phillips (5 out of 5 patents)

5. Shengjiang Tu (5 out of 6 patents)

IDiyas Innovation Intelligence. (2026). Company Patent Portfolio: Insitro, Inc.. Retrieved from https://idiyas.com/company/insitro

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]

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