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Learn about LLNL’s innovative platform for rapid computational design and experimental validation of antibody therapeutics and vaccines.

LLNL biologists, computer scientists and engineers are developing a machine-learning enabled computational/experimental framework that enables unprecedented acceleration of the drug development process.

Our platform integrates world-class high-performance computing and simulation, machine learning, structural bioinformatics, and multi-objective optimization to perform in silico antibody and antigen design and optimization. These designs are then experimentally validated at LLNL and at our partner facilities, and results are either pursed as candidate medical countermeasures (MCMs) or provided as critical feedback for computational redesign.

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We are partnering with academia, and industry to further develop and demonstrate this capability. When fully implemented, our system will enable both a proactive and rapid reactive approach for designing MCMs.

If you are interested in joining our team or have questions, please contact us at CPDB [at]

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Omicron Sprint

Following reports in December 2021 that SARS-CoV-2 variants Omicron BA.1 and BA.1.1 escaped the clinical AZD1061 antibody (COV2-2130), we launched our platform to design COV2-2130 mutants that simultaneously restored binding to both BA.1 and BA.1.1, while maintaining strong binding to the co-circulating Delta variant.

In under 3 weeks, we used our platform to propose 376 antibody variants. Experimental evaluation confirmed enrichment across our design goals, with our top candidate potently neutralizing wildtype SARS-CoV-2 and the Delta, BA.1, BA.1.1, and BA.2 variants.

A depiction of the mutation position of  AbBERT: Learning Antibody Humanness Via Masked Language Modeling

AbBERT: Learning Antibody Humanness Via Masked Language Modeling

One component of our computational pipeline is a model to understand the degree of similarity of novel, engineered antibody sequences to typical human antibody sequences. Accurate understanding of the degree of humanness can help reduce or manage the risk of failure modes such as immunogenicity or poor manufacturability. AbBERT was developed to fill this need as a transformer-based language model derived from ProtBert, which was trained on up to 20 million unpaired sequences from the Observed Antibody Space database.

We validated AbBERT using a novel “multi-mask'' scoring procedure to demonstrate high accuracy in predicting antibody complementary determining regions—including the challenging hypervariable H3 region. We then demonstrated several uses of AbBERT on downstream antibody design and prediction tasks. AbBERT enhances in silico antibody optimization via deep reinforcement learning by utilizing its learned embeddings as additional observations during optimization.

Researchers use a mix of lab science and machine learning to optimize antibodies.

We have career opportunities to develop a next generation computational antibody and antigen design platform driven by simulation and machine learning models trained on large experimental datasets.

Lawrence Livermore National Laboratory has been ranked among the top 10 employers in the San Francisco Bay Area by Forbes Magazine, won multiple Glassdoor Best Places to Work awards, and is among the top 12 in government services nationwide. The Laboratory offers employees outstanding careers with a first-class benefits package in a beautiful location. See our Careers page for more information about our culture, the benefits and perks, our history, and job opportunities.

dan faissol


Dan Faissol, PhD

Principal Investigator

faissol1 [at] (Email)

shankar sundaram


Shankar Sundaram, PhD

Program Executive

sundaram1 [at] (Email)

Tom Bates


Tom Bates, PhD

Program Deputy

bates13 [at] (Email)

Dawn Whalen


Dawn Whalen

Operations Manager

whalen3 [at] (Email)

Kate Arrildt


Kate Arrildt, PhD

Virology, Vaccinology, LLNL

Thomas Bunt


Thomas Bunt

Rapid Response Laboratory Lead

Tom Desautels


Tom Desautels, PhD

Multi-fidelity simulation and machine learning for efficacy prediction

Ed Lau


Ed Lau, PhD

Molecular dynamics simulation

Brenden Petersen


Brenden Petersen, PhD

Deep reinforcement learning, multi-objective optimization, system integration

Brent Segelke


Brent Segelke, PhD

Molecular microbiologist and protein crystallographer

Fangqiang Zhu


Fangqiang Zhu, PhD

Molecular dynamics simulation, structural antibody modeling

Adam Zemla


Adam Zemla, PhD

Protein structure bioinformatics

Drew Bennett


Drew Bennett, PhD

Molecular dynamics simulation, multi-scale modeling

Jose Cadena Pico


Jose Cadena Pico, PhD

Machine learning

Matt Coleman


Matt Coleman, PhD

Vaccinologist, biochemical engineer

Jeff Drocco


Jeff Drocco, PhD


Andre Goncalves


Andre Goncalves, PhD

Machine learning, multi-task learning, multi-objective optimization

John Goforth


John Goforth

Software development lead

Emilia Gzesiak


Emilia Grzesiak

Biostatistics and data science, software development

Rebecca Haluska


Rebecca Haluska

Software development and database engineering

Tek Huang Lee


Tek Hyung Lee, PhD

Synthetic and systems biologist and microbial engineer

Jayaraman Thiagarajan


Jay Jayaraman Thiagarajan, PhD

Deep learning

Xander Ladd


Xander Ladd, PhD

Machine learning, Bayesian Optimization

Mikel Landajuela


Mikel Landajuela, PhD

Deep reinforcement learning

Leno Da Silva


Leno Da Silva, PhD

Deep reinforcement learning

Jacky Lo


Jacky Lo

Biochemical assay development specialist

Nate Mundhenk


Nate Mundhenk, PhD

Deep learning, Convolutional Neural Networks

Priyadip Ray


Priyadip Ray, PhD

Statistical learning

Dante Ricci


Dante Ricci, PhD

Molecular genetics, systems biology

Bonnee Rubinfeld


Bonnee Rubinfeld

Cellular, molecular, micro - biologist/biochemist

Ed Saada


Ed Saada, PhD

Pathogen interactions and medical countermeasures

Mary Silva


Mary Silva

Bayesian Statistics and Data Science

Cheryl Strout


Cheryl Strout

Biochemical and analytical scientist

Camilo Valdes


Camilo Valdes, PhD


Denis Vashchenko


Denis Vashchenko

Deep learning language modeling, software development

Dina Weilhammer


Dina Weilhammer, PhD

Immunologist, virologist

Tracy Weisenberger


Tracy Weisenberger

Cellular, molecular, and viral biologist

Jiachen Yang


Jiachen Yang, PhD

Reinforcement learning, machine learning

Boya Zhang


Boya Zhang, PhD

Machine learning, surrogate-based modeling

Interested in joining the cutting edge of computational design and bioengineering?

If you’re interested in joining our team or have questions, contact us at CPDB [at]