Sustainable Biomining of Rare Earth Elements

We are exploring environmentally friendly biomining techniques that can efficiently extract, purify and recycle rare-earth elements (REEs) — critical minerals that play an essential role in clean energy and defense technologies. LLNL scientists developed a protein-based biomaterial that enables REE extraction and separation from non-traditional feedstocks, such as end-of-life consumer electronics. They continue exploring ways to increase the technique’s efficiency, providing a sustainable, low-cost alternative to conventional, solvent-based REE separation methods.

AI Accelerates Search for New Disease Treatments

Researchers from LLNL, Stanford and UCLA are using AI to find potential treatments for ALS and other neurodegenerative diseases by repurposing existing drugs prescribed for other conditions, significantly reducing timelines to deliver new solutions. By analyzing ALS patients’ health records, they identified three drug classes that positively affected survival, suggesting that they could be good repurposing candidates. LLNL bioscience researchers are also accelerating development of cancer drugs, leveraging the Lab’s supercomputers, AI tools and a unique drug discovery platform to model thousands of compounds and rapidly identify promising candidates with the potential to block tumor growth.

GUIDE Team Furthers Progress Toward Combating Viral Evolution

In 2024, the Generative Unconstrained Intelligent Drug Engineering (GUIDE) team used machine learning to restore the efficacy of antibody drugs. Recently, the team as made further progress in combating viral evolution by enhancing an antibody called AZD3152 — a prophylactic for the immunocompromised — using deep mutational scanning on the SARS-CoV-2 antigen. They identified the antibody’s vulnerabilities and, using computational antibody design that integrates structure-based modeling and machine-learning, they optimized AZD3152 against 44 existing and potential COVID variants.