Precision Medicine Redefined with [SqAAS]
nCode is pioneering the first platform to offer Sequence as a Service, acting as a strategic catalyst in the creation of programmable mRNA therapeutics. Our innovative technology delivers precise, multi-functional, and potentially curative sequences, transforming the landscape of precision medicine. By harnessing the power of advanced AI and deep learning, we are making the future of personalized healthcare a reality. Welcome to the forefront of next-generation RNA therapeutics with nCode.
28+
Publications
4+
Patents
25+
Team

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Why nCODE?
By leveraging the nCode platform, you gain access to cutting-edge technologies, expert support, and a comprehensive suite of tools designed to accelerate and optimize the development of RNA therapeutics.

Interdisciplinary
Expertise
Founders published in Science, cover of Molecular Cell and trained at Nobel prize winning labs at MIT, UCSF & Harvard
Founders have successful exits in the software industry having developed state of the art software
Access to global Thinkhat talent with multiple AI products in the market.

Accelerated
Development
Sequences provided in as little as 3 months from the initiation of contract
SQAAS (Sequence As A Service) offered as milestone driven contracts that Pay for Performance (P4P) only
Customized validation, Qualification Assay development for target sequences per client’s requirements.

Unique
Strategy
Continuous learning based AI Engine “the Synapse” that refines sequence generation iteration over iteration
Leveraging the whole stack of central dogma with comprehensive omics including mass spec
Selective, highly efficient as well as immunogenic sequences provided.

Predictive
Accuracy
Immunogenicity Forecasting: Advanced AI models forecast immunogenicity, minimizing the risk of adverse immune responses.
Target Cell Specificity: Deep learning algorithms predict how sequences will interact with specific cell types, ensuring targeted and effective treatments.
Drug Interaction Predictions: Our platform anticipates potential drug interactions, aiding in the design of safer and more effective therapies.

Synapse
Data
(01)
Comprehensive
Gather a diverse dataset of mRNA sequences from multiple databases, ensuring comprehensive coverage. Preprocess the data by encoding sequences and annotating key regions such as the CDS, 5' UTR, and 3' UTR.
Learn
(02)
Train
Train our deep learning model-Synapse, on the prepared dataset to recognize and learn patterns within the sequences. Employ cross-validation and hyperparameter tuning to optimize the model's performance and generalizability.
Experiment
(03)
Validation
Use the trained model to generate new mRNA sequences, then experimentally test these sequences in vitro to evaluate their stability, translation efficiency, and biological activity. Apply experimental feedback to identify areas for improvement in sequence design.
Refine
(04)
Accuracy
Incorporate the experimental results and feedback into the model's training process, refining the model to improve its sequence generation capabilities. Iteratively train the model with updated data, creating a continuous learning loop that enhances sequence quality and functionality over time.