Dante Ricci

Dr. Dante Ricci, a scientist in the Synthetic Biology group at Lawrence Livermore National Laboratory (LLNL), is at the forefront of revolutionizing medical countermeasure development through artificial intelligence (AI). With recent funding from ARPA-H, Ricci and his team aims to harness AI for the rapid and effective design of antibodies and antibody-like molecules, potentially transforming biologic discovery.

Ricci's research addresses significant challenges in de novo antibody design, such as the sparseness and heterogeneity of publicly available antibody/antigen datasets, the difficulty in generating new datasets, and the substantial computing resources required to build models. The team’s strategy involves systematizing and accelerating protein model development by generating fundamental knowledge about data requirements for protein design. This work aims to build a platform for high-throughput characterization and prediction/design of transient protein-protein interactions, culminating in the computational design of a potent synthetic biologic targeting a pathogen based on antigen information.

Ricci's diverse research portfolio at LLNL includes designing ecologically and genetically stable GMOs, developing protein-based biologics for critical material recovery, and creating microbes that enhance plant growth and carbon capture. He has also worked on high-resolution dissection of pathogenic mechanisms, building automated pipelines for antibody-encoding DNA constructs, and developing autonomous experimentation systems for continuous biological research.

The rapid advancements in AI have profoundly impacted de novo antibody design. "AI is changing everything in molecular biology, and quickly," Ricci notes. AI-enabled models working with biological sequence data have accelerated biological discovery and biotechnological development. Despite the promise of these models, Ricci emphasizes that they are only as good as the data used to train them. To meet this challenge, his team is investing in producing rigorous, standardized, high-resolution datasets and scaling data production processes to expand the available antibody data. This "data factory" approach represents a paradigm shift in R&D, making it an exciting time for AI-based biodesign.

Collaboration is crucial to Ricci's work. Internally, his team includes molecular biologists, microbiologists, protein biochemists, synthetic biologists, engineers, and computer scientists. Externally, they collaborate with world leaders in antibody design and discovery from academia, government, and the private sector. "Success here will require active, iterative interplay between computer scientists who train models and nominate designs, and experimentalists who collect training data, validate model predictions, and develop the methods that give access to the swaths of biological information that data-hungry AI models require for de novo protein design," Ricci explains. LLNL's unique environment promotes broad interdisciplinary collaboration and provides unmatched computational capabilities, making it an ideal setting for this ambitious work.

Ricci's journey to LLNL began with a serendipitous interaction during his postdoc at Stanford, leading to a collaboration with LLNL scientists. His subsequent experiences in the private sector, including at Achaogen and Federation Bio, taught him valuable lessons about strategic thinking, nimbleness, and the challenges of for-profit research.

At LLNL, Ricci appreciates the team-based culture, which minimizes frictional cross-functional communication. "The culture here is eminently team-based—it feels to me as if collaboration is woven into the fabric of the institution," he says. The relatively flat structure promotes interaction and accessibility, while the "big ideas" culture incentivizes bold, disruptive solutions.

Ricci is passionate about the potential of AI to transform drug discovery, envisioning a future where potent, safe neutralizing antibodies can be identified within a week of detecting a pandemic pathogen. "Imagine a future where a pandemic pathogen is detected and a potent, safe neutralizing antibody that can prevent and/or treat infection is identified within a week!" he explains.

To achieve that ambitious future, Ricci looks forward to advanced facilities that can accommodate interdisciplinary staff and include automation, modularization, and scalable experimentation. The growing emphasis on biosciences at LLNL, amplified by the national response to the COVID-19 pandemic, makes it an exciting time for growth and innovation in the field. Lawrence Livermore National Laboratory tackles the entire life cycle of biological challenges—from awareness to prevention, preparedness to detection, and response to recovery—to develop mission-driven solutions.