Smarter Biosensors for Myocardial Injury — Powered by AI-Engineered Aptamers
AI-Powered Aptamers for Cardiac Biosensors
Enhancing troponin detection in next-gen cardiac biosensors with precision-designed aptamers
Why Biosensors for Myocardium?
Myocardial injury
is the leading global cause of mortality.
Troponins are key indicators
but current antibody-based detection faces challenges (stability, cost, cold storage).
Biosensors + aptamers =
portable, affordable, and stable detection tools for point-of-care and continuous monitoring.
Applications in Troponin Testing
Point-of-care diagnostics
portable biosensors for ER / ambulances
Portable field testing
military, sports medicine, remote care
Wearable cardiac monitoring devices
continuous troponin tracking
R&D partnerships
captamer-enabled biosensor prototypes for CROs and medtech developers
Why Choose Our AI-Powered Aptamer Design for Troponin Diagnostics?
Faster Development
AI reduces design time from months to weeks
High Sensitivity & Specificity
Machine learning-optimized aptamers for unmatched target binding
Versatile Applications
Electrochemical, optical, and diagnostic biosensors
Cost-Effective
Reduce R&D costs with in silico aptamer screening
How Our AI-Driven Aptamer Design Works
✅ Result: 50% faster development and 10-100x higher sensitivity vs. traditional SELEX methods
Target Analysis & Sequence Profiling
AI Target Binding Prediction: Our algorithms analyze the 3D structure, charge distribution, and biochemical properties of your target molecule (protein, small molecule, cell marker).
Epitope Mapping: Machine learning identifies optimal binding regions (e.g., hydrophobic pockets, charged residues) to maximize aptamer affinity.
In Silico Aptamer ScreeningIn Silico Screening
Generative AI Design: We use neural networks to generate millions of potential aptamer sequences tailored to your target.
Affinity & Specificity Scoring: ML models rank sequences based on:
Binding energy – Predicts strongest interactions.
Cross-reactivity risk – Filters out sequences that may bind non-specifically.
Stability – Checks for nuclease resistance (critical for diagnostic biosensors).
Lab Validation & Optimization
High-Throughput Screening (HTS): Top AI-predicted aptamers are synthesized and tested via:
Aptamer biosensors use synthetic DNA/RNA strands (aptamers) as bioreceptors to detect targets like proteins, toxins, or pathogens. They offer: ✔ Higher stability than antibodies (work in extreme pH/temperature). ✔ Reusability (regenerate binding sites easily). ✔ Customizability (easy chemical modification for sensor integration).
Traditional SELEX
AI-Driven Design
Takes 6+ months
Weeks to design
High lab costs
Reduces wet-lab experiments by 70%
Limited sequence diversity
Explores billions of sequences in silico
Yes! They are cheaper, faster, and portable for:
Medical diagnostics (e.g., cardiac biomarkers, viral detection).
Food safety (e.g., Salmonella, mycotoxins).
Environmental monitoring (e.g., heavy metals in water).
Our AI-optimized aptamers achieve picomolar (pM) sensitivity—comparable to antibodies. Example:
COVID-19 spike protein: Detected at 0.1 pM in saliva.
Glucose monitoring: Continuous detection in <5 sec.
We provide pre-functionalized aptamers with:
Thiol groups (for gold electrodes).
Biotin tags (for streptavidin-coated surfaces).
Fluorescent dyes (for optical detection).
Yes! Unlike antibodies, aptamers:
Survive freeze-thaw cycles.
Last years at room temperature (lyophilized form).
Resist denaturation in harsh conditions.
Absolutely. We engineer aptamers with:
Signal reporters (e.g., methylene blue for electrochemical sensors).
Polymer backbones (e.g., 2’-fluoro for nuclease resistance).
Quencher pairs (e.g., for FRET-based detection).
Request a One-Pager: AI-Powered Aptamers for Cardiac Biosensors
Neoncorte Bio is at the forefront of the convergence between artificial intelligence and aptamer design. Our team comprises experts in computational biology, bioinformatics, and machine learning, all driven by a mission to accelerate innovation in protein design. By leveraging our advanced AI models, we provide unparalleled software and tools that enhance efficiency, reduce costs, and push the boundaries of what's possible in protein engineering
Publications
Explore our contributions to the forefront of biotechnology and artificial intelligence. From AI-driven aptamer design 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 AI applications and software engineering tailored to the life sciences, we specialize in solving complex challenges and delivering innovative software & tools 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:
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.