Antibody Prediction and Generation

Problem Biotech research teams often work with antibodies, which, like all proteins, are sequences of amino acids connected by peptide bonds. Each amino acid can be represented with a one-letter code, making it possible to store the primary structure of proteins as strings. The challenge for our client was twofold: first, to predict the animal species an antibody originated from based on its amino acid sequence; and second, to go further by generating new antibody sequences conditioned on a chosen biological species. Standard bioinformatics tools were not sufficient for solving both predictive and generative tasks efficiently at scale.

Result The prediction model demonstrated high accuracy in determining antibody origin, giving researchers a reliable tool for organizing and analyzing experimental data. The generative model opened new opportunities for drug discovery and biomedical research, enabling the creation of species-specific antibodies in silico. The client received a scalable platform that combined advanced machine learning with practical scientific applications, reinforcing their position at the forefront of bioinformatics.

How did we achieve it? We designed and trained two complementary AI models. The first was a predictive model capable of analyzing amino acid sequences and accurately determining the species of origin for each antibody. The second was a generative model that produced entirely new antibody sequences while respecting biological constraints and allowing control over the target species. Both models were trained on large datasets of protein sequences.

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