Linnaeus Bioscience
Linnaeus Bioscience Inc. is dedicated to accelerating drug discovery, especially in the fight against antibiotic resistant bacteria. Founded in 2012, it develops innovative technologies such as bacterial cytological profiling supported by AI to discover and characterize new antibacterial molecules. The company collaborates with various partners to develop solutions for antimicrobial research and development.
Industries
Nr. of Employees
small (1-50)
Linnaeus Bioscience
San Diego, California, United States, North America
Products
Image-based bacterial cytological profiling platform
A microscopy-based phenotypic profiling platform that generates quantitative cytological readouts to determine antimicrobial MOA, support hit triage, and provide SAR insights.
AI-driven mycobacterial cytological profiling platform
A mycobacteria-optimized cytological profiling platform integrated with AI to classify MOA for Mycobacterium species and compare novel compounds to a mycobacterial phenotype database.
Fungal cytological profiling platform
Adaptation of phenotypic cytological profiling methods to fungal pathogens to determine antifungal MOA and support early-stage antifungal discovery.
Bacteriophage assays
A variety of services aimed at phage testing, potency, susceptibility, time-to-lysis assays, mechanisms of action, and phage discovery.
Minimum Inhibitory Concentration Testing
Minimum Inhibitory Concentration (MIC) assays identify the lowest concentration of a compound that successfully inhibits visible growth of a microorganism using broth microdilution methods following CLSI guidelines.
AI Platform for Tuberculosis
Comprehensive AI platform capable of correctly identifying the mechanism of action for virtually all classes of antibiotics effective against M. tuberculosis.
Image-based bacterial cytological profiling platform
A microscopy-based phenotypic profiling platform that generates quantitative cytological readouts to determine antimicrobial MOA, support hit triage, and provide SAR insights.
AI-driven mycobacterial cytological profiling platform
A mycobacteria-optimized cytological profiling platform integrated with AI to classify MOA for Mycobacterium species and compare novel compounds to a mycobacterial phenotype database.
Fungal cytological profiling platform
Adaptation of phenotypic cytological profiling methods to fungal pathogens to determine antifungal MOA and support early-stage antifungal discovery.
Bacteriophage assays
A variety of services aimed at phage testing, potency, susceptibility, time-to-lysis assays, mechanisms of action, and phage discovery.
Minimum Inhibitory Concentration Testing
Minimum Inhibitory Concentration (MIC) assays identify the lowest concentration of a compound that successfully inhibits visible growth of a microorganism using broth microdilution methods following CLSI guidelines.
AI Platform for Tuberculosis
Comprehensive AI platform capable of correctly identifying the mechanism of action for virtually all classes of antibiotics effective against M. tuberculosis.
Services
Image-based phenotypic profiling to determine antimicrobial mechanism of action, bin screening hits by MOA, generate SAR data, and monitor target engagement during lead optimization.
Specialized profiling and AI classification for mycobacterial species to determine MOA, compare phenotypes to a mycobacterial phenotype library, and support tuberculosis drug discovery projects.
Minimum inhibitory concentration assays using broth microdilution following CLSI guidelines across bacterial and fungal pathogens with customization of growth media and sample types.
A suite of antimicrobial assays including time-kill kinetics, checkerboard synergy testing, cellular ATP bioenergetic assays, and biofilm eradication testing to characterize compound activity.
Image-based cytological profiling and antifungal assay development for fungal pathogens to characterize MOA and support lead selection.
Potency and susceptibility testing, high-throughput phage–bacteria assays, time-to-lysis kinetics, phage MOA studies, receptor identification, resistance frequency assessment, and phage–antibiotic synergy testing.
Image-based phenotypic profiling to determine antimicrobial mechanism of action, bin screening hits by MOA, generate SAR data, and monitor target engagement during lead optimization.
Specialized profiling and AI classification for mycobacterial species to determine MOA, compare phenotypes to a mycobacterial phenotype library, and support tuberculosis drug discovery projects.
Minimum inhibitory concentration assays using broth microdilution following CLSI guidelines across bacterial and fungal pathogens with customization of growth media and sample types.
A suite of antimicrobial assays including time-kill kinetics, checkerboard synergy testing, cellular ATP bioenergetic assays, and biofilm eradication testing to characterize compound activity.
Image-based cytological profiling and antifungal assay development for fungal pathogens to characterize MOA and support lead selection.
Potency and susceptibility testing, high-throughput phage–bacteria assays, time-to-lysis kinetics, phage MOA studies, receptor identification, resistance frequency assessment, and phage–antibiotic synergy testing.
Expertise Areas
- Mechanism-of-action determination
- Image-based phenotypic profiling
- Antimicrobial assay development
- High-throughput screening and hit triage
Key Technologies
- Fluorescence microscopy
- Quantitative live-cell imaging
- High-content, image-based phenotypic profiling
- Deep learning / computer vision for image analysis
News & Updates
Funding received in September 2024 to support MycoBCP platform for studying Mycobacteroides.
Funding received in 2022 for screening compounds against Mycobacteroides.
Funding received in 2019 for developing BCP for Mycobacteroides.
Recognition received in 2017 for research on BCP.
A publication involving Dr. Diana Quach and colleagues on using deep learning with BCP.
A 2022 study on antibacterial activity involving Dr. Diana Quach.
Funding received in September 2024 to support MycoBCP platform for studying Mycobacteroides.
Funding received in 2022 for screening compounds against Mycobacteroides.
Funding received in 2019 for developing BCP for Mycobacteroides.
Recognition received in 2017 for research on BCP.
A publication involving Dr. Diana Quach and colleagues on using deep learning with BCP.
A 2022 study on antibacterial activity involving Dr. Diana Quach.