Rapid Computational Design & Experimental Validation of Antibodies
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.
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] llnl.gov (CPDB[at]llnl[dot]gov)
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, PhD
Principal Investigator
Shankar Sundaram, PhD
Program Executive
Tom Bates, PhD
Program Deputy
John Hernandez
Business Project Manager
Kate Arrildt, PhD
Virology, Vaccinology
Thomas Bunt
Rapid Response Laboratory Lead
Barry Chen, PhD
Language Modeling Lead
Simone Conti, PhD
Molecular Dynamics Simulations
Jie Deng
Software and Data Architect
Tom Desautels, PhD
Multi-fidelity Simulation and Machine Learning for Efficacy Prediction
John Goforth
Software Development Lead
Mikel Landajuela, PhD
Optimization Lead
Ed Lau, PhD
Molecular Dynamics Simulation
Ed Saada, PhD
Pathogen Interactions and Medical Countermeasures
Ana Paula Sales, PhD
Developability Prediction Lead
Brent Segelke, PhD
Structural Biologist, Protein Biochemist
Adam Zemla, PhD
Protein Structure Bioinformatics
Fangqiang Zhu, PhD
Molecular Dynamics Simulation, Structural Antibody Modeling
Aram Avila-Herrera
Bioinformatics Software Developer
Drew Bennett, PhD
Molecular Dynamics Simulation, Multi-scale Modeling
Jose Cadena Pico, PhD
Machine Learning
Ippolito Imani Caradonna
Automation Expert
Rudrasis Chakraborty
Researcher
Matt Coleman, PhD
Vaccinologist, Biochemical Engineer
Zachary Cosenza
Bayesian Modeling, Multi-task Learning
Aneesha Devulapally
ChemBio Data Scientist
Jeff Drocco, PhD
Bioinformatics
Andre Goncalves, PhD
Machine Learning, Multi-task Learning, Multi-objective Optimization
Emilia Grzesiak
Biostatics and Data Science, Software Development
Rebecca Haluska
Software Development and Database Engineering
Conor Hayes, PhD
Reinforcement Learning, Machine Learning
Matthew Jemielita
Assay Dev Lead
Piyush Karande, PhD
Deep Learning
Tek Hyung Lee, PhD
Synthetic and Systems Biologist and Microbial Engineer
Leno De Silva, PhD
Deep Reinforcement Learning
Tavish McDonald
Machine Learning, Language Modeling
Yamen Mubarka
Deep Learning
Nate Mundhenk, PhD
Deep learning, Convolutional Neural Networks
Rami Musharrafieh
Vaccines Lead
Christine Ocampo
Operation Administrator
Kim Phan
Administrative Specialist
Hiranmayi Ranganathan, PhD
Deep Active Learning, Machine Learning
Tiffany Reck
Administrative Specialist
Dante Ricci, PhD
Molecular Genetics, Systems Biology
Natalie Rowland
Administrative Specialist
Bonnee Rubinfeld
Cellular, Molecular, Micro-biologist/Biochemist
Mary Silva
Bayesian Statistics and Data Science
Cheryl Strout
Biochemical and Analytical Scientist
Gareth Trubl, PhD
Viral and Microbial Ecologist
Camilo Valdes, PhD
Bioinformatics
Denis Vashchenko
Deep Learning Language Modeling, Software Development
Yaqing Wang, PhD
Structural Biologist, Molecular Biophysicist
Dina Weilhammer, PhD
Immunologist, Virologist
Tracy Weisenberger
Cellular, Molecular, and Viral Biologist
Boya Zhang, PhD
Machine learning, surrogate-based modeling
Interested in joining the cutting edge of computational design and bioengineering?
CPDB [at] llnl.gov (Contact us )
Predictive Design of Biologics is a collaborative effort spearheaded by two Lawrence Livermore National Laboratory organizations:
