Sequence based drug design

High-throughput antibody design using NGS and bigdata analysis

Why Molcure?

Biomolecule big data and AI algorithm for biomolecule discovery / engineering

  • NGS combination

    100
    patterns

  • Antibody big-data

    300M
    leads

  • AI design know-how

    4000
    patterns

  • Consensus frame extraction

    Extraction of consensus sequences from antibody DNA sequences that is pooled in native blood
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  • Translation method

    Translate massive NGS DNA sequence data with avoiding errors such as frame-shifting
  • Preprocessing method

    Merge NGS pair-end sequence data using QC appending algorithm for extracting highly reliable sequences
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  • Affinity prediction based on amplification rate

    Cluster amplification rate:
    Calculate normalized amplification rate from pre&post-panning NGS data to make binding affinity index.
  • Clustering method

    Increase accuracy of mapping sequence on antibody space via clustering based on edit distance. Extract important motifs for binding by internal cluster analysis
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  • Training data

    Predicting non-specific antibody sequence by using machine learning algorithm with FDA approved antibody sequence and Molcure’s own non-specific antibody database
  • Feature extraction for machine learning

    Extract from antibody sequences for machine learning.
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Machine learning algorithms

Develop classifiers via SVM /Random forest / DNN

  • DNN


  • RANDOM FOREST


  • SVM