Hyperdimensional Stochastic Optimization for High-Throughput Affinity Maturation (HSO-HTAM) in Single B Cell Sequencing

**Abstract:** This paper introduces Hyperdimensional Stochastic Optimization for High-Throughput Affinity Maturation (HSO-HTAM), a novel framework for accelerating antibody affinity maturation in single B cell sequencing workflows. Existing methods rely on computationally expensive iterative cycles of mutation and selection, limiting throughput. HSO-HTAM leverages hyperdimensional computing (HDC) combined with a stochastic optimization engine to drastically reduce the cycle time by efficiently exploring the vast antibody sequence space. This approach provides significant advantages for both research and industrial antibody development, enabling rapid generation of high-affinity antibodies against challenging targets, with the potential to dramatically lower discovery costs and accelerate lead generation timelines. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*