Alchemab Selected to Access NVIDIA Cambridge-1 Supercomputer to Advance Machine Learning Enabled Antibody Discovery
Boston, Massachusetts and Cambridge, UK March 22, 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.
Attend Alchemab’s session on deciphering the language of antibodies on March 24 at GTC, a free to register global AI conference. Find more details on the Nvidia Inception program here. Find project updates and more information on Cambridge-1 projects here.
About Alchemab
Alchemab has developed a highly differentiated platform which enables the identification of novel drug targets, therapeutics and patient stratification tools by analysis of patient antibody repertoires. The platform uses well-defined patient samples, deep B cell sequencing and computational analysis to identify convergent protective antibody responses among individuals that are susceptible but resilient to specific diseases.
Alchemab is building a broad pipeline of protective therapeutics for hard-to-treat diseases, with an initial focus on neurodegenerative conditions and oncology. The highly specialized patient samples that power Alchemab’s platform are made available through valued partnerships and collaborations with patient representative groups, biobanks, industry partners and academic institutions.
For more information, visit www.alchemab.com.
Contact
Jonathan Nugent
Stern Investor Relations
212-362-1200