TECHNOLOGY LICENSING OPPORTUNITY: SurfGraphPro
Open Special Notice Posted
- Solicitation number
- S-196061
- Agency
-
Department of Energy
- Responses due
- Set-aside
- No set-aside
Opportunity facts
- NAICS code
- 541715 Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
- PSC
- AJ11 General Science And Technology R&D Services; General Science And Technology; Basic Research
- Place of performance
- Los Alamos, New Mexico 87545, United States
- Points of contact
-
- Mike Erickson licensing@lanl.gov
Notice details come from SAM.gov. Updated .
About this opportunity
The Department of Energy issued a technology licensing opportunity for SurfGraphPro, an AI tool developed by Los Alamos National Laboratory that converts complex protein structures into graph-based representations for accelerated analysis, with responses due by December 5, 2026.
SurfGraphPro addresses computational bottlenecks in protein research by combining 3D surface graph representations with machine learning to reduce analysis runtime and improve scalability while preserving structural detail. The tool enables researchers to identify binding sites, predict molecular interactions, and understand protein behavior more efficiently than traditional computational approaches, which typically rely on hand-selected biochemical features, expensive calculations, or narrow task-specific models with limited generalization.
Applications span drug discovery, antibody design, pathogen detection, and custom protein engineering. By converting protein surfaces into graph-based representations that preserve both physical geometry and biochemical information from surface-exposed amino acids, SurfGraphPro eliminates the need for repeated manual feature engineering or specialized analysis pipelines for each new use case.
This opportunity falls under NAICS 541715 (Research and Development in Physical, Engineering, and Life Sciences) and PSC AJ11 (General Science and Technology R&D Services). No set-aside applies.
Notice text
SurfGraphPro, an AI tool, transforms complex protein structures into an easy-to-analyze format that helps researchers quickly identify binding sites, predict molecular interactions and understand protein behavior with greater speed and scalability than traditional approaches. By combining 3D surface graph representations with advanced machine learning, this technology from Los Alamos National Laboratory reduces the computational burden of protein analysis while preserving the structural detail needed for high-value applications in drug discovery, antibody design, pathogen detection and custom protein engineering.
The Challenge:
Protein surfaces are extremely complex, three-dimensional structures, and that complexity makes them difficult to analyze using traditional computational approaches. In practice, many existing methods rely on hand-selected biochemical features, expensive calculations or narrow task-specific models that do not generalize well to new questions. As a result, researchers can face slow runtimes, limited scalability and incomplete insight when trying to identify binding sites, predict molecular interactions or understand broader protein behavior across large sets of proteins. These limitations can make it difficult to move quickly from protein structure data to useful predictions in areas such as drug discovery, antibody design, pathogen detection and protein engineering.
Problems Solved:
SurfGraphPro solves these problems by converting protein surfaces into a graph-based representation that preserves both the physical shape of the surface and the biochemical information carried by surface-exposed amino acids. This tool gives machine learning models a more efficient and flexible way to process protein structures without requiring repeated manual feature engineering or highly specialized analysis pipelines for each new use case. By reducing computational burden while keeping the key structural details needed for prediction, SurfGraphPro makes it possible to analyze proteins more quickly, at larger scale and across a wider range of applications. This approach includes identifying likely binding sites, estimating molecular compatibility, supporting drug screening efforts, improving antibody and protein design workflows, and enabling other protein-focused prediction tasks where speed, scalability and adaptability matter.
Key Advantages:
- Helps turn complex protein structures into information computers can use more easily
- Speeds up protein analysis compared with older approaches
- Reduces the amount of manual feature engineering needed
- Preserves important details about both protein shape and chemical properties
- Supports multiple uses, including drug discovery, antibody design and pathogen detection
- Offers a flexible platform that can be adapted to different protein-related tasks
Market Applications:
- Pharmaceuticals and Biotechnology (drug discovery, target screening, protein optimization)
- Biologics and Antibody Development (antibody engineering, binding analysis, therapeutic design)
- Diagnostics and Infectious Disease (pathogen detection, biomarker analysis, assay development)
- Agricultural Biotechnology (protein analysis, crop trait research, bio-based product development)
- Materials and Industrial Science (protein-mineral interaction studies, polymer compatibility, bio-inspired materials)
- Research Tools and Software (protein modeling, computational biology, prediction platforms)
Development Status: TRL 3
US Patent pending
LA-UR-26-23589
LANL Tech Partnerships: Unlock the Innovative Potential
Los Alamos National Laboratory offers a wide range of cutting-edge technologies and capabilities that may provide your company with a competitive edge in the market and unlock the innovative potential that can enhance, refine, and revolutionize your products.
LANL’s licensing program focuses on moving inventions developed by our researchers to commercial innovations. Patented and patent pending inventions and copyrighted software are available to existing and start-up companies through exclusive and non-exclusive licensing agreements. For specific discussions, please contact licensing@lanl.gov.
Note: This is not a call for external services for the development of this technology.
https://www.lanl.gov/engage/collaboration/feynman-center/partner-with-us/licensing-technology
m.lanl.gov/tech-search
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