Transforming Biologics with AI-Powered Solutions
Revolutionize Antibody Discovery with Machine Learning
Our cutting-edge machine learning platform accelerates the discovery, optimization, and development of high-affinity antibodies, enabling breakthroughs in diagnostics, therapeutics, and research. Experience faster, more accurate antibody discovery like never before
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Neoncorte Bio
Where ML Meets Biotechnology
Neoncorte Bio is at the forefront of the convergence between machine learning and protein & enzyme engineering. Our team comprises experts in computational biology, bioinformatics, and machine learning, all driven by a mission to accelerate innovation in enzyme design. By leveraging our advanced AI models, we provide unparalleled solutions that enhance efficiency, reduce costs, and push the boundaries of what's possible in protein engineering & enzyme engineering
Our services
Comprehensive Antibody Discovery and Development Solutions
Antibody Discovery
Antibody Optimization
ML-Driven Epitope Mapping
Applications
Our ML-driven platform delivers antibodies for a variety of industries, including:
- TherapeuticsCancer immunotherapy, autoimmune disease treatment, and more
- DiagnosticsHigh-specificity antibodies for biomarker detection
- Research ToolsReliable antibodies for academic and commercial use
Technology: How ML Drives Antibody Discovery
Our proprietary ML algorithms analyze vast datasets, predict antibody-antigen interactions, and streamline the discovery process. By leveraging machine learning, we identify the most promising candidates, reducing trial-and-error and accelerating development.
Our proprietary ML algorithms analyze vast datasets, predict antibody-antigen interactions, and streamline the discovery process. By leveraging machine learning, we identify the most promising candidates, reducing trial-and-error and accelerating development.
Optimization of cellulase variants
Frequently Asked Questions (FAQs)
Our solution leverages state-of-the-art artificial intelligence and machine learning to design and optimize antibodies from scratch. By simulating antigen-antibody interactions and predicting optimal binding regions, our platform accelerates the discovery and development of high-affinity, specific antibodies for therapeutic, diagnostic, and research applications.
Our services are ideal for biotechnology and pharmaceutical companies, academic research labs, and diagnostic developers. Whether you’re working on immunotherapy, vaccine development, or diagnostic assay innovation, our platform can help streamline your antibody discovery process and reduce R&D timelines.
The process starts with your input data—such as antigen structure, target epitopes, and desired binding properties. Our AI algorithms then generate a diverse library of antibody sequences and predict their binding affinities using molecular simulations and deep learning models. We prioritize the most promising candidates for further experimental validation, significantly reducing the number of costly lab iterations.
Our platform can design a variety of antibodies, including monoclonal antibodies, bispecific/multispecific antibodies, and antibody fragments (such as Fab or scFv). The system is flexible and can be customized to meet the specific requirements of your therapeutic or diagnostic application.
Absolutely. We tailor our AI models to your target molecules, experimental constraints, and specific design goals. Our team works closely with you to adjust algorithm parameters and integrate your proprietary data, ensuring that our solution meets your unique research or commercial objectives.
Our platform features robust API support and standard data export formats (e.g., CSV, JSON) to seamlessly integrate with your Laboratory Information Management Systems (LIMS) and computational pipelines. Additionally, we offer comprehensive onboarding and training to ensure smooth adoption by your team.
Our models are trained on extensive datasets of antigen-antibody interactions and refined through continuous feedback from experimental validations. While our predictions are highly reliable, we recommend conducting in vitro or in vivo validation to confirm the functional performance of selected antibody candidates.
Yes, we provide proof-of-concept (PoC) trials and collaborative validation support. Our experts can help you set up experiments to test top candidate antibodies in your lab, ensuring that our computational predictions translate effectively into high-performance molecules.
We offer flexible pricing tailored to the scope of your project. Options include subscription-based models for ongoing research needs and project-based pricing for specific antibody discovery campaigns. Please contact us for a detailed consultation and custom quote.
Our dedicated customer support team is available to assist you at every stage—from onboarding and technical training to troubleshooting and updates. We offer multiple support channels, including email, phone, and live chat, as well as comprehensive documentation and FAQs.
Partner with Us to Accelerate Your Antibody Discovery
Ready to leverage the power of ML for your next antibody project? Let’s work together to revolutionize therapeutics and diagnostics
Publications
Explore our contributions to the forefront of biotechnology and artificial intelligence. From ML-driven enzyme engineering to deep learning applications in data analysis, our publications highlight innovative solutions to complex challenges in molecular biology and computational science.
- Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5′-3′ exonuclease activity, effective deoxyuracil incorporation, and tolerance to locked nucleic acid (LNA)-containing substrates.
- The transcriptomic data are being frequently used in the research of biomarker genes of different diseases and biological states. The most common tasks there are the data harmonization and treatment outcome prediction. Both of them can be addressed via the style transfer approach. Either technical factors or any biological details about the samples which we would like to control (gender, biological state, treatment, etc.) can be used as style components.
- List of all Neoncorte Bio publications dedicated to Molecular Biology, Biotechnology, Artificial Intelligence and Artificial Neural Networks, published mostly by Nikolay Russkikh, CEO of Neoncorte Bio
Our Expertise in Action
With extensive experience in ML applications and software engineering tailored to the life sciences, we specialize in solving complex challenges and delivering innovative solutions for our customers. Our work demonstrates a deep understanding of cutting-edge technologies and their application in the real world.
Here are examples of the types of projects we have successfully delivered:
Here are examples of the types of projects we have successfully delivered:
- Automated NGS Data Analysis:Designed a production-grade solution for the automated processing, annotation, and analysis of Next-Generation Sequencing (NGS) data.
- Single-Cell Data Integration:Built state-of-the-art tools for integrating multimodal single-cell data, achieving recognition for technical excellence.
- Metagenomic Classification Algorithms:Developed advanced methods for classifying sequencing reads in metagenomics research.
- High-Throughput Image Processing Pipelines:Engineered an efficient pipeline to process millions of sequencing images with exceptional accuracy.
- Cell Counting via AI:Created a computer vision solution for precise cell counting in microphotography images, streamlining data analysis.
Get in touch with us
Phone: +1-503-754-3958 in US
Email: contact@neoncorte.com
Email: contact@neoncorte.com
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