Brand Stories
Antibody Discovery with AI: Faster, Smarter Drug Design
Artificial intelligence (AI) and machine learning (ML) are having a significant impact on how the biopharma industry develops new therapeutic antibodies. What historically has been a slow, trial-and-error-based process, mostly carried out at a lab bench, is now becoming a fast, data-driven discipline using in silico research methods and computational power.
The impact of AI is seen at the earliest stages of antibody discovery, giving researchers the ability, for example, to screen millions of antibody sequences and predict key properties like diversity in antibody sequences, number of unique variants available for screening, and panning and selection, accelerating the early stage of the drug discovery pipeline.
Thanks to iterative loops, where in silico predictions guide wet lab experiments and lab results improve the models, researchers can achieve higher precision and efficiency in therapeutic development.
At the heart of this transformation is data. To be successful, AI models require high-quality, scientifically robust and well-managed training data, and while an increase in the use of synthetic data is helping to bridge data gaps, access to real-world data remains essential.1
It is for this reason that lab informatics tools, including electronic lab notebooks (ELNs) and laboratory information management systems (LIMS), are critical, acting as connectors across teams, technologies and data types, centralizing and contextualizing experimental data and enabling seamless integration with AI models. This approach creates a continuous feedback cycle where AI refines lab tests, and the results power the next round of predictions.
How AI is accelerating early stages of antibody development
While AI is having an impact across large areas of antibody discovery, there are three areas that are seeing some of the most exciting progress: structure prediction, antibody-antigen interaction modeling and optimization. Tools like AlphaFold2 and RoseTTAFold have demonstrated the ability to predict protein structures from amino acid sequencing, and while these were major steps forward, antibody discovery brings specific challenges, especially in the CDR regions that determine how they bind to antigens.2–3
However, new antibody-specific tools like IgFold are using language models trained on hundreds of millions of antibody sequences to speed up the identification of successful candidates.4
In antibody-antigen interaction modeling, we are beginning to see AI help us with the design of antibodies with high specificity for the target antigen. AI systems like AlphaProteo use a combination of structure prediction and deep learning to model how an antibody will interact with a target, often without knowing the precise binding site in advance, to generate new protein binders.5
One of the most exciting examples of this is the ESM-IF1 model developed by researchers at Stanford.6 This model does not rely on labeled experimental data sets but uses the 3D backbone of a protein structure to predict sequences that are likely to fold into shape. In one study, the team used ESM-IF1 to improve two SARS-CoV-2 antibodies. Although they tested only 30 variants, they achieved up to 25-fold improvements in neutralization potency and 37-fold increases in binding affinity, representing a completely different way of doing antibody optimization.
The third area where we are seeing AI positively impact antibody discovery is in optimization. Once researchers have identified a promising antibody, they need to improve binding strength and reduce off-target effects; this ensures it’s stable and, importantly, manufacturable.
Tools, such as the Protein Repair One-Stop Shop (PROSS), can optimize protein sequences by designing mutations while preserving function and improving structural stability.7 AI models can simulate mutation and screening cycles in silico.
Challenges to AI adoption in antibody discovery
With any transformative technology, there are a number of major hurdles to be addressed to make visible the important impact of AI in antibody discovery. The largest and most important of these is data.8 Scientific AI models rely on the data on which they are trained to be accurate, specific, and organized.
While there are enormous libraries of antibody sequences, that data is often missing structural or functional context that is required to ensure accurate predictions.9 Private data sets tend to be siloed across teams or restricted due to IP concerns, and public data sets are often broadly distributed and lack standardization. These issues make it harder to train AI models on robust data sets, an issue that is compounded with rare or novel targets where data is even more limited.
Another challenge is the complexity involved in AI models. Antibodies have incredibly diverse structures, particularly in relation to how they bind with other molecules, and that diversity creates complexity when modeling. This lack of transparency makes it harder for researchers to trust the output or to troubleshoot when things go wrong.
Furthermore, there is still a lag between in silico research and wet lab validation. While AI can identify or design thousands of candidates, the wet lab cannot scale at the same pace, and even with the improvements in high-throughput screening, there can be a bottleneck.
Ensuring AI success through lab informatics
To unlock the full potential of AI in antibody discovery, it is important to go beyond the models themselves to examine the infrastructure on which they depend, specifically modern platform-based lab informatics solutions. This is the digital backbone of a lab, including ELNs, LIMS, scientific data management systems (SDMS) and workflow tools, which collectively structure, capture and connect the diverse and complex data that is generated across biopharma R&D, from initial antibody screening to manufacturability.
Science-aware data is central to the efficacy of AI in research, but it’s not solely about the quantity of the data available; it’s important that the data is well annotated, consistent, contextual and accessible. It is essential that this data accurately reflects scientific and biological realities. A modern LIMS can ensure that experimental data, historically found in fragmented formats or with inconsistent metadata, is managed in a standardized environment that ensures it can be seamlessly integrated from the lab to the AI model.
Additionally, a modern lab informatics platform can help to address the bottleneck between in silico and the wet lab. Informatics platforms can help the lab to scale by automating data capture, managing experiment queues and integrating sample tracking with experimental results. This allows wet lab scientists to prioritize the most promising candidates early and minimize redundant experimentation.
A third key benefit is how lab informatics supports the lab-in-the-loop model, a concept that is central to how AI and experimental science can work together. In a lab-in-the-loop model, AI operates as part of a continuous cycle, generating hypotheses, informing experimental design and learning from the results. AI models can create a closed and intelligent feedback loop where real-world results guide in silico predictions.
Conclusion
From modeling structure and binding to optimization, AI is accelerating the science of antibody discovery as the industry shifts towards smarter and more data-driven workflows.
However, the real impact comes when AI is paired with the right lab infrastructure. Without high-quality and contextual data and the systems to manage it, there is a risk that AI cannot achieve its full potential. That is where modern lab informatics platforms excel. They form the digital backbone ensuring data integrity, traceability and scientific context. They reduce human error and ensure data are reproducible, which is key for training effective AI models.
Ultimately, it is the lab-in-the-loop model – where predictions guide experiments and outcomes inform the next round – that unlocks the full potential of AI in antibody discovery. It is not just about accelerating timelines but about enabling a smarter, more adaptive approach to drug discovery. With the right lab informatics foundation, AI becomes more than a tool – it becomes a catalyst for continuous improvement across the entire antibody development lifecycle.
Brand Stories
‘Cruising is booming:’ Why luxury hotel brands are launching lavish cruise ships | Exclusive
Ritz-Carlton and Four Seasons are two of the world’s most renowned and expensive and hotel companies.
But forget staying in their hotel rooms – they’re among the top travel brands taking to the water.
And Waldorf Astoria – which is owned by Hilton – is the latest travel firm to strike out, launching a luxury Nile cruise in 2026.
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More akin to mega yachts and much smaller than regular cruise ships these vessels hold just a few hundred cashed-up guests.
Ritz Carlton recently launched its third ship, Luminara, with an A-list filled party.
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Models Kendall Jenner and Naomi Campbell, TV host Martha Stewart, and actors Orlando Bloom and Kate Hudson were among those invited to the extravagant party.
Outside of hotels, on-the-ground tour company Trafalgar announced it is also expanding into river cruising with two new ships, the Trafalgar Verity and Trafalgar Reverie, for sailings on the Rhine and Danube rivers, starting in April 2026.
Ted Blamey Principal at specialist cruise consulting firm CHART Management Consultants says there are many reasons all these firms want in on the water-bound holidays.
“The first is basically that cruising is booming, so it’s a great opportunity for experienced travel and accommodation companies to capitalise on,” he tells 9Travel.
“Second, I guess, would be, that these organisations, they have very powerful existing guest basis.
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“They have a very significant number of past guests who are loyal to the brand, and love it, and why not offer them something new that will continue to get their loyalty and of course, earn revenues.
“I guess another reason is that these same people are open to new experiences.”
Meanwhile he said cruising is unique from a business point of view because guests are captive on the vessel much of the time.
And that means you can control their holiday – as well as retain much of the money they pay to be there.
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The new players are competing against other luxury cruise brands such as Crystal Crusies, Ponant, Explora Journeys, Azamara, Silversea, and Regent Seven Seas.
But this could be good for the whole industry Ted says.
“I think all of us in the industry have felt for years that competition is a good thing, it grows the market,” he says.
Even Orient Express, most famous for its lavish trains, is getting involved. It’s planning the world’s largest sailing ship, Orient Express Silenseas, for next year.
Smaller Swiss brand, Aman is also setting sail.
Meanwhile, images show the first vessel for Four Seasons won’t be anything like normal cruiser.
The yacht will have an extendable marina on both sides for water sports, swimming or simply posing for Instagram photos.
Captain Kate McCue has jumped ship from Celebrity Cruises to captain it.
But one thing all the vessels will have in common is that their high-net-worth guests can enjoy the finest things the world can offer.
That includes an almost one to one crew member to guest ratio, fine dining meals from top chefs and lavish suites with huge terraces.
Prices are not always widely advertised but run into the tens of thousands, making a trip something everyday Aussie cruises can only dream of.
Private islands and exclusive destinations you can visit on cruises
MONTPELIER, Vt. (WCAX) – A Vermont lawmaker has been selected to co-lead a national task force on artificial intelligence policy.
Bradford Democratic Rep. Monique Priestley co-chairs the task force with a Republican representative from Utah.
She says her focus is to learn more about how AI impacts consumer protection and data policy.
“Right now, AI is touching everything that we are interacting with. It’s used in software that determines if you can get a loan, if you can get an apartment, or whether or not you qualify for different education. Your health care is largely impacted by artificial intelligence,” Priestley said.
The task force will connect lawmakers with expert voices in the industry and create a first-of-its-kind bipartisan state AI policy memo to guide policymaking across the country.
Copyright 2025 WCAX. All rights reserved.
From April through June, the tourism and travel industries grappled with several political challenges at once: President Donald Trump’s “Liberation Day” tariff turbulence. Messy debates over the “One Big Beautiful Bill.” U.S. travel bans and declining tourism from abroad.
In response, many of the nation’s biggest airlines, hotels, travel service companies, and associated trade associations spent bigger-than-usual amounts to lobby Congress and the Trump administration, according to a Skift analysis of new federal lobbying disclosure documents filed Monday.
This government influence spending, which includes money spent on both in-house and for-hire lobbyists in Washington, D.C., is designed to defend industry and corporate interests and advocate for favorable policies and legislation.
Among the notable revelations:
Where Spending Rose
Trade Groups: The U.S. Travel Association reported a spike in its lobbying activity during the second quarter ($1.03 million) versus a year earlier ($900,00).
It was also well beyond what it spent during the same period in 2021 during Joe Biden’s first year as president ($840,000) and in 2017 during the first year of Trump’s first term ($640,000).
“Lobbying expenditures during the first year of a new presidential administration or new Congress typically increase — along with legislative and regulatory action — compared to the previous year,” U.S. Travel Association spokesperson Spencer
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