BOSTON & CAMBRIDGE, ENGLAND-- March 23, 2022 -- Alchemab Therapeutics, a biotechnology company focused on the discovery and development of naturally-occurring protective antibodies and immune repertoire-based patient stratification tools, has been selected by NVIDIA to harness the power of the UK’s most powerful supercomputer, Cambridge-1. Alchemab will use the NVIDIA DGX SuperPOD supercomputing cluster, powered by NVIDIA DGX A100 systems, to gain greater understanding and insights from its extensive neurology and oncology datasets.

“We are honored to collaborate with NVIDIA to advance our work applying machine learning to the prediction of antibody structure and function,” said Douglas A. Treco, PhD, Chief Executive Officer of Alchemab Therapeutics. “Using Cambridge-1, Alchemab will vastly accelerate our capabilities and we are excited about the potential to collaborate with NVIDIA’s world-leading team to better understand the language of antibodies.”

Craig Rhodes, EMEA Industry Lead for Healthcare and Life Sciences at NVIDIA, commented: “Cambridge-1 enables the application of machine learning to help solve the most pressing clinical challenges, advance health research through digital biology, and unlock a deeper understanding of diseases. The system drives workloads that are scaled and optimised for supercomputing and will help extraordinary organisations like Alchemab, a member of the NVIDIA Inception program, to further their research on antibodies and other protective therapeutics for hard to treat diseases.”

“Our collaboration with NVIDIA will unlock countless opportunities to advance Alchemab’s state-of-the-art platform, facilitating the discovery of novel therapeutics and patient stratification techniques,” said Jake Galson, PhD, Head of Technology at Alchemab Therapeutics. “Machine learning is accelerating research across multiple therapeutic areas and will be pivotal in helping Alchemab predict the function of novel antibodies based on their sequence alone.”

An individual’s antibody repertoire encodes information about past immune responses and potential for future disease protection. Alchemab believes that deciphering information stored in these antibody sequence datasets will transform the fundamental understanding of disease and enable discovery of novel diagnostics and antibody therapeutics. Using self-supervised machine learning, Alchemab has developed antibody-specific language model AntiBERTa (Antibody-specific Bi-directional Encoder Representation from Transformers), a 12-layer transformer model which provides a contextualized numeric representation of antibody sequences. AntiBERTa learns biologically relevant information and is primed for multiple downstream tasks which are improving our understanding of the language of antibodies.