Our Pipeline
At Katamaran Industries, we are harnessing the power of our AI-driven super engine to build a cutting-edge pipeline of potential first-in-class.
We focus on indications with high unmet need, including oncology and rare disease.
Computational Lab
AI Engine 1 : KAtamaran’s Predictive AI engine (KAPA ™)
KAtamaran’s Predictive AI engine begins by developing AI models trained on curated public datasets in oncology and drug response spanning pharmacogenomic screens,transcriptomic atlases, and multi-omic tumor databases
These datasets establish the foundation for models that predict drug efficacy, resistance mechanisms, biomarker associations, and patient subpopulation responses.
● Engineered micro-tumors and immune co-cultures for mechanistic studies
● 3D patient-derived organoids for primary drug screening
This closed feedback loop continuously evolves our AI—integrating real human biological data to improve predictive accuracy, prioritize drug candidates, and identify responders with greater precision.
Once validated on public data, these models are progressively optimized using human relevant experimental data from Katamaran’s Wet Lab, including:
Multi-omic embeddings linking compounds to phenotypes and responder signatures
Predictive filters for ADMET, selectivity, and off-target risk
Generative chemistry (SMILES / graph-based diffusion models) for novel molecule creation
Our models integrate
Reinforcement learning guided by experimental feedback from Katamaran’s Wet Lab (3D organoids, ex-vivo slices, Lab-on-Chip assays)
●Lab-on-Chip and Trial-on-Chip assays for translational and patient-matched validation
AI Engine 1:Katamaran’s Generative AI for
Molecular Advancement(GAMA™)
KAtamaran’s Predictive AI engine begins by developing AI models trained on curated public datasets in oncology and drug response spanning pharmacogenomic screens,transcriptomic atlases, and multi-omic tumor databases
Description | Target | Diease Indication | Late Discovery | Preclinical | Phase 1/2 | Pivotal/Phase 3 |
|---|---|---|---|---|---|---|
Oncology | ||||||
KAT-001 | PKD1 | |||||
KAT-002 | PKD2 | |||||
KAT-003 | Undisclosed | |||||
Rare Diseases | ||||||
REC-4881 | MEK1/2 |
Description | Target | Diease Indication | Late Discovery | Preclinical | Phase 1/2 | Pivotal/Phase 3 |
|---|---|---|---|---|---|---|
Oncology | ||||||
KAT-001 | PKD1 | |||||
KAT-002 | PKD2 | |||||
KAT-003 | Undisclosed | |||||
Rare Diseases | ||||||
REC-4881 | MEK1/2 |
Katamaran is establishing a next-generation Wet Lab facility that integrates 3D patient derived tumor systems, microfluidic Lab on Chip technologies, and AI-driven analytics.
Currently in development, the lab will serve as the experimental foundation of Katamaran’s precision oncology platform. where patient biology, automation, and machine learning converge.
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Wet Lab
Key capabilities under build-out include:
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02
03
04
05
Patient-Derived Organoids (PDOs)/ Mini-Tumors
Engineered Organoids
Organotypic Tumor Slices
Lab-on-Chip (LoC)
Trial-on-Chip (ToC)
High-fidelity ex-vivo validation models preserving full tumor architecture.
Miniaturized microfluidic assays that recreate the tumor microenvironment under controlled perfusion and gradients, enabling dynamic studies of drug action and immune-tumor interactions.
A miniature, clinical-trial-like platform using patient-matched tumor slices to validate drug responses, optimize dosing, and predict responders before clinical trials.
Currently in development, the lab will serve as the experimental foundation of Katamaran’s precision oncology platform. where patient biology, automation, and machine learning converge.
Key capabilities under build-out include:
PDOs enhanced with stromal, endothelial, or immune components and CRISPR-based edits.
Mini-tumors representing individual patient genetics and heterogeneity.
Mini-tumors representing individual patient genetics and heterogeneity.
Patient-Derived Organoids (PDOs)/ Mini-Tumors
01
PDOs enhanced with stromal, endothelial, or immune components and CRISPR-based edits.
Engineered Organoids
02
High-fidelity ex-vivo validation models preserving full tumor architecture.
Organotypic Tumor Slices
03
Miniaturized microfluidic assays that recreate the tumor microenvironment under controlled perfusion and gradients, enabling dynamic studies of drug action and immune-tumor interactions
Lab-on-Chip (LoC)
04
A miniature, clinical-trial-like platform using patient-matched tumor slices to validate drug responses, optimize dosing, and predict responders before clinical trials.
Trial-on-Chip (ToC)
05
This facility will enable AI-linked 3D drug screening, immune-oncology co-cultures, and predictive response modeling, bridging discovery and clinic with human-relevant data.
Our goal is to launch the first functional LoC and ex-vivo assay units within the next development phase.
This facility will enable AI-linked 3D drug screening, immune-oncology co-cultures, and predictive response modeling, bridging discovery and clinic with human-relevant data.
Our goal is to launch the first functional LoC and ex-vivo assay units within the next development phase
We’re looking for collaborators, investors, and visionary scientists. Reach out and be part of the mission.
Quick Links
Our Address
India :
Unit, #603, 6th floor, Sigmasoft tech park, Gamma block, Whitefeild, Bengaluru, Karnataka, 560066
US :
Unit 419,180 talmadge rd, Edison nj 08817
UK :
41 Clooney Terrace, Londonderry, Northern Ireland, BT47 6AP
Mail :
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© 2025 Katamaran Industries. Innovating for global health equity.
Automated Wet Lab Workflows (Planned Development)
Katamaran is planning the development of a next-generation automated wet lab integrating robotics, sterile modular systems, and AI-driven workflow orchestration to reduce manual handling, variability, and human error in complex biological experiments.
The envisioned setup will feature enclosed, sterile automation modules connected by robotic or conveyor-based transfer systems, enabling culture plates and samples to move seamlessly through stages of cell seeding, compound dosing, incubation, imaging, and analysis.
The planned automation stack will include:
01
02
03
04
Robotic liquid handling and dispensing systems for precise and reproducible multi-condition screening
Automated incubation and imaging units for consistent environmental control and real-time data acquisition
AI-based workflow orchestration software to coordinate assays, monitor performance, and ensure traceability
Integrated data pipelines linking results directly with KAPA™ and GAMA™ for continuous model improvement
Automation
Automated Wet Lab Workflows (Planned Development)
Katamaran is planning the development of a next-generation automated wet lab integrating robotics, sterile modular systems, and AI-driven workflow orchestration to reduce manual handling, variability, and human error in complex biological experiments.
The envisioned setup will feature enclosed, sterile automation modules connected by robotic or conveyor-based transfer systems, enabling culture plates and samples to move seamlessly through stages of cell seeding, compound dosing, incubation, imaging, and analysis.
By minimizing manual intervention and experimental error, Katamaran aims to establish a high-throughput, continuously learning research ecosystem that enhances data reliability and accelerates translation from prediction to proof-of-concept.
The planned automation stack will include:
01
Robotic liquid handling and dispensing systems for precise and reproducible multi-condition screening
02
Automated incubation and imaging units for consistent environmental control and real-time data acquisition
03
AI-based workflow orchestration software to coordinate assays, monitor performance, and ensure traceability
04
Integrated data pipelines linking results directly with KAPA™ and GAMA™ for continuous model improvement
Once implemented, these automated workflows will form a closed, self-learning discovery cycle, where AI designs, automation executes, and biology validates
By minimizing manual intervention and experimental error, Katamaran aims to establish a high-throughput, continuously learning research ecosystem that enhances data reliability and accelerates translation from prediction to proof-of-concept.
Once implemented, these automated workflows will form a closed, self-learning discovery cycle, where AI designs, automation executes, and biology validates
Our Platform
AI Engine 1 : KAtamaran’s Predictive AI engine (KAPA ™)
KAtamaran’s Predictive AI engine begins by developing AI models trained on curated public datasets in oncology and drug response spanning pharmacogenomic screens,transcriptomic atlases, and multi-omic tumor databases
These datasets establish the foundation for models that predict drug efficacy, resistance mechanisms, biomarker associations, and patient subpopulation responses.
● Engineered micro-tumors and immune co-cultures for mechanistic studies
● 3D patient-derived organoids for primary drug screening
This closed feedback loop continuously evolves our AI—integrating real human biological data to improve predictive accuracy, prioritize drug candidates, and identify responders with greater precision.
Once validated on public data, these models are progressively optimized using human relevant experimental data from Katamaran’s Wet Lab, including:
Multi-omic embeddings linking compounds to phenotypes and responder signatures
Predictive filters for ADMET, selectivity, and off-target risk
Generative chemistry for novel molecule creation
Our models integrate
Reinforcement learning guided by experimental feedback from Katamaran’s Wet Lab (3D organoids, ex-vivo slices, Lab-on-Chip assays)
●Lab-on-Chip and Trial-on-Chip assays for translational and patient-matched validation
AI Engine 2 : Katamaran’s Generative AI for
Molecular Advancement(GAMA™)
KAtamaran’s Predictive AI engine begins by developing AI models trained on curated public datasets in oncology and drug response spanning pharmacogenomic screens,transcriptomic atlases, and multi-omic tumor databases
Together, KAPA™ and GAMA™ form Katamaran’s Twin AI Engines—uniting prediction and generation to power the future of precision drug discovery
Over time, GAMA™ becomes a self-learning discovery engine, accelerating hit-to-lead timelines and expanding therapeutic innovation across oncology and rare diseases
Through this closed feedback loop, GAMA™ continuously evolves designing, optimizing,and prioritizing new molecules and biologics guided by human-relevant data.
Built on large, curated chemical, bioactivity, and multi-omic datasets, GAMA™ learns the
underlying chemical grammar, structure–activity relationships, and biological response patterns that drive therapeutic efficacy