Accelerate your therapeutics pipeline with Antibody AnalyticsBased on the comprehensive Antibody Knowledge Graph, we develop analytics solutions that connect the dots and provide insights into antibody developability, immunogenicity binding, and other features. Researchers use our solutions to quickly discover the advantages of individual candidate antibodies, stratify outputs of Next-Generation Sequencing (NGS) or phage display, and - most importantly - enhance their therapeutics pipelines thanks to data-driven insights into antibodies.Learn more
Our state-of-the-art homology modeling solution can be applied to individual molecules and entire repertoires originating from NGS. We combine modeling with our structural database and extend the scope of available templates via regular updates to develop a robust toolkit.
We employ a combination of structural analysis and natural profiling to identify antibodies and antibody parts that might carry the risk of immunogenicity. We query molecules for the naturally observed amino acid diversity from millions of sequences in NGS. We flag positions that don’t fall within the natural distribution as risky and display them in three-dimensional space to reveal potentially immunogenic patches.
We developed a statistical profiler that contrasts a query sequence to successful antibody therapeutics in clinical trials and extracts of information from patents. By contrasting a query antibody to such an extract, researchers get information about the discrepancies of their candidates concerning the physicochemical features of successful therapeutics.
We analyze naturally sourced NGS to identify commonalities in sequences across multiple studies, which indicate consistently observed mutations. These provide an opportunity to quantify the allowed malleability of antibodies and generate insights on derisking antibody sequences from the developability and immunogenicity perspectives.
Our platform offers a toolkit of basic antibody bioinformatics operations such as antibody numbering, delineation of CDR regions, sequence alignment based on numbering schemes, and Vh/Vl pairing compatibility.
We have developed deep learning models covering paratope, epitope prediction, and docking. These tools will become available later in 2021 after thorough benchmarking.