Pin-Kuang Lai
speaker
67 appearances
1 recordings
1 series
first heard Jul 2026
last heard 23 Jul
Pin-Kuang Lai’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.
Appearances
What has been particularly encouraging is the breadth of contributors.
We've seen work from academia, industry, and technology developers reflecting the collaborative nature of modern antibody research.
Another positive outcome is that the collection has moved beyond simply asking whether AI has a role in antibody development.
That question has largely been answered.
The discussion is now focused on how to build more accurate
interpretable and deploybable.
models that can genuinely
Uh
accelerate drug development.
Of course the field is evolving rapidly.
No single collection can capture everything, but I believe we've established or a s uh a valuable foundation that researchers will continue to build upon.
Well, uh rather than pointing to a single paper, what I find most exciting is the diversity of the approaches pre uh represented.
Some papers focus on antibody sequence and structure prediction, while others address developability properties such as stability, aggregation, viscosity, and manufacturability.
There are also contributions applying generative AI, large language models, foundation models, and advanced deep learning techniques to antibody engineering.
Another aspect I appreciate is that several studies emphasize integrating computational prediction with experimental validation.
Those hybrid approaches are particularly compelling because they produce models that are not only accurate, but also more trustworthy and biologically meaningful.
Together I believe these papers illustrate how AI is becoming an integral part of antibody research that the uh rather than a standalone computational exercise.
Uh there are several things emerged repeatedly.
The first is the rapid adoption of deep learning and foundation models for antibody sequence and structure analysis.
The second is a growing emphasis on developability prediction.
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