Topic: Biology/Biomedicine

A new cancer drug candidate developed by LLNL, BridgeBio Oncology Therapeutics, and the Frederick National Laboratory for Cancer Research has demonstrated the ability to block tumor growth without triggering a common and debilitating side effect.

A team of researchers from LLNL, Stanford University, and UCLA are using AI and machine learning to find potential treatments for amyotrophic lateral sclerosis and other neurodegenerative diseases in the form of drugs prescribed for other conditions.

Researchers from LLNL, in collaboration with other leading institutions, have successfully used an AI-driven platform to preemptively optimize an antibody to neutralize a broad diversity of SARS-CoV-2 variants.

A study led by LLNL scientists is providing new insights into the complex interactions between proteins and cell membranes, combining detailed molecular simulations and large-scale models.

The latest episode of the Big Ideas Lab podcast investigates the use of artificial intelligence for drug discovery and other uses.

The Generative Unconstrained Intelligent Drug Engineering (GUIDE) program accelerates development of medical countermeasure candidates to redefine biological defense.

A virtual reality training platform allows liver surgeons to prepare and train outside of the operating room.

The collaboration has enabled expanding systems of the same architecture as LLNL’s upcoming exascale supercomputer, El Capitan, featuring AMD’s cutting-edge MI300A processors.

In a milestone for supercomputing-aided drug design, LLNL and BridgeBio Oncology Therapeutics today announced clinical trials have begun for a first-in-class medication that targets specific genetic mutations implicated in many types of cancer.

LLNL’s HPC capabilities play a significant role in international science research and innovation, and Lab researchers have won 10 R&D 100 Awards in the Software–Services category in the past decade.

In a groundbreaking development for addressing future viral pandemics, a multi-institutional team involving LLNL researchers has successfully combined an AI-backed platform with supercomputing to redesign and restore the effectiveness of antibodies whose ability to fight viruses has been compromi

Thirteen students traveled to Livermore in early December for a computer science course simulating pond ecology and evolution.

LLNL is participating in the 35th annual Supercomputing Conference (SC23), which will be held both virtually and in Denver on November 12–17, 2023.

With this year’s results, the Lab has now collected a total of 179 R&D 100 awards since 1978. The awards will be showcased at the 61st R&D 100 black-tie awards gala on Nov. 16 in San Diego.

The event brought together 35 University of California students—ranging from undergraduates to graduate-level students from a diversity of majors—to work in groups to solve four key tasks, using actual electrocardiogram data to predict heart health.

The new model addresses a problem in simulating RAS behavior, where conventional methods come up short of reaching the time- and length-scales needed to observe biological processes of RAS-related cancers.

Combining specialized software tools with heterogeneous HPC hardware requires an intelligent workflow performance optimization strategy.

Highlights include MFEM community workshops, compiler co-design, HPC standards committees, and AI/ML for national security.

Presented at the 2022 International Conference on Computational Science, the team’s research introduces metrics that can improve the accuracy of blood flow simulations.

Kevin McLoughlin has always been fascinated by the intersection of computing and biology. His LLNL career encompasses award-winning microbial detection technology, a COVID-19 antiviral drug design pipeline, and work with the ATOM consortium.

From molecular screening, a software platform, and an online data to the computing systems that power these projects.

Highlights include power grid challenges, performance analysis, complex boundary conditions, and a novel multiscale modeling approach.

Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.

The SAMRAI library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with structured adaptive mesh refinement.

Highlights include response to the COVID-19 pandemic, high-order matrix-free algorithms, and managing memory spaces.