Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries.

Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an end-to-end Bayesian, language model-based method for designing large and diverse libraries of high-affinity single-chain variable fragments (scFvs) that are then empirically measured.

A dataset comprised of binding interactions for 104,972 antibodies against a SARS-CoV-2 peptide.

The dataset presented here contains quantitative binding scores of scFv-format antibodies against a SARS-CoV-2 target peptide collected via an AlphaSeq assay that can be used in the development and benchmarking of machine learning models. Starting from three seed sequences identified from a phage display campaign using a human naïve library, four sets of 29,900 antibodies were designed in silico by creating all k = 1 mutations and random k = 2 and k = 3 mutations throughout the complementary-determining regions (CDRs). Of the 119,600 designs, 104,972 were successfully built in to the AlphaSeq library and target binding was subsequently measured with 71,384 designs resulting in a predicted affinity value for at least one of the triplicate measurements.

A dataset to facilitate automated workflow analysis.

Data sets that provide a ground truth to quantify the efficacy of automated algorithms are rare due to the time consuming and expensive, although highly valuable, task of manually annotating observations. These datasets exist for niche problems in developed fields such as Natural Language Processing (NLP) and Business Process Mining (BPM), however it is difficult to find a suitable dataset for use cases that span across multiple fields, such as the one described in this study. The lack of established ground truth maps between cyberspace and the human-interpretable, persona-driven tasks that occur therein, is one of the principal barriers preventing reliable, automated situation awareness of dynamically evolving events and the consequences of loss due to cybersecurity breaches.