AWS and Hopkins Engineering announce groundbreaking database for AI/ML antibody design
Captured source
source ↗AWS and Hopkins Engineering announce groundbreaking database for AI/ML antibody design - Amazon Science
Close
Close
Social
bluesky
threads
youtube
github
rss
Menu
Research
Research areas
Automated reasoning
Cloud and systems
Computer vision
Conversational AI
Economics
Information and knowledge management
Machine learning
Operations research and optimization
Quantum technologies
Robotics
Search and information retrieval
Security, privacy, and abuse prevention
Sustainability
Our scientific contributions
Publications
Research from our scientists and collaborators.
Conferences
Our experts present and discuss cutting-edge research at scientific meetings globally.
Research areas
Automated reasoning
Cloud and systems
Computer vision
Conversational AI
Economics
Information and knowledge management
Machine learning
Operations research and optimization
Quantum technologies
Robotics
Search and information retrieval
Security, privacy, and abuse prevention
Sustainability
Our scientific contributions
Publications
Research from our scientists and collaborators.
Conferences
Our experts present and discuss cutting-edge research at scientific meetings globally.
News & blog
The latest from Amazon researchers
Amazon Science Blog
Technical deep-dives and perspectives from our scientists.
News
Research milestones and recent achievements.
The latest from Amazon researchers
Amazon Science Blog
Technical deep-dives and perspectives from our scientists.
News
Research milestones and recent achievements.
Collaborations
Amazon Research Awards
Overview
Call for proposals
Latest news
Research stories
Recipients
Amazon Nova AI Challenge
Overview
Rules
FAQs
Teams
Research collaborations
Overview
Carnegie Mellon University
Columbia University
Hampton University
Howard University
IIT Bombay
Johns Hopkins University
Max Planck Society
MIT
Tennessee State University
University of California, Los Angeles
University of Illinois Urbana-Champaign
University of Southern California
University of Texas at Austin
Virginia Tech
University of Washington
Amazon Research Awards
Overview
Call for proposals
Latest news
Research stories
Recipients
Amazon Nova AI Challenge
Overview
Rules
FAQs
Teams
Research collaborations
Overview
Carnegie Mellon University
Columbia University
Hampton University
Howard University
IIT Bombay
Johns Hopkins University
Max Planck Society
MIT
Tennessee State University
University of California, Los Angeles
University of Illinois Urbana-Champaign
University of Southern California
University of Texas at Austin
Virginia Tech
University of Washington
Resources
Code and datasets
AGI Labs
Meet the team building useful AI agents.
Amazon Nova
Try Amazon’s frontier foundation models.
Code and datasets
AGI Labs
Meet the team building useful AI agents.
Amazon Nova
Try Amazon’s frontier foundation models.
Careers
Careers
Explore our open roles.
Amazon Scholars
Faculty research opportunities on industry-scale technical challenges.
Postdoctoral Science Program
Early-career research opportunities alongside experienced industry scientists.
Careers
Explore our open roles.
Amazon Scholars
Faculty research opportunities on industry-scale technical challenges.
Postdoctoral Science Program
Early-career research opportunities alongside experienced industry scientists.
Search
Submit Search
Machine learning
AWS and Gray Lab at Johns Hopkins Whiting School of Engineering announce groundbreaking database for AI/ML antibody design
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets, enabling transparent performance evaluation for AI-guided antibody design.
By Staff writer
April 14, 2026
10 min read
Share
Share
Copy link
X
Line
QZone
Sina Weibo
分享到微信
x
Overview by Amazon Nova
AWS and Johns Hopkins Engineering have launched the Antibody Developability Benchmark, a large-scale, diverse dataset for evaluating AI-guided antibody design. The dataset includes 50 seed antibodies with four structural formats targeting 42 antigens, measuring six key developability traits. It features engineered variants with both favorable and unfavorable developability outcomes, validated through wet-lab experiments. The benchmark supports zero-shot learning, allowing models to be evaluated without prior exposure to the dataset, enhancing confidence in results. The benchmark results are now available as part of Amazon Bio Discovery ; additional benchmarks will be added over time and released in a paper later this year.
Was this answer helpful?
In 1986 the US Food and Drug Administration issued its first approval for human use of a therapeutic antibody. Despite steady advances in methodology, genetic sequencing, and biomedical science, 40 years later the process of discovering and optimizing therapeutic antibodies often remains prohibitively expensive, in terms of both cost and time. Recent experiences with pandemic-style infectious-disease outbreaks lend an even greater urgency to the need to more quickly and efficiently identify and develop these antibodies. Artificial-intelligence- and machine-learning-guided approaches to antibody design, in the form of biological foundation models (BioFM), represent a significant opportunity to address these challenges. Models built using protein language models (pLMs) and structure-based deep-learning frameworks have significant potential to predict antibody developability properties — the characteristics that determine whether a molecule is manufacturable, stable, and safe as a therapeutic. The development of those tools could drastically shorten discovery timelines while also reducing experimental costs. That potential, however, has been hindered by the lack of a public dataset that would allow researchers to benchmark those tools, a crucial step in the development of trustworthy in-silico tools for drug discovery. While there are existing public antibody datasets, they are too frequently limited by a focus on a single antibody format or target. Others are composed of naturally occurring or clinically advanced antibodies, a bias that severely limits their utility for training or evaluating predictive models. “Trust in the predictions made by these models must be grounded in evaluations against experimental data that is…
Excerpt shown — open the source for the full document.
Notability
notability 7.0/10Notable database for AI/ML antibody design
Amazon (Nova) has a writing signal matching data demand, evals and quality.