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
Thank you for having me.
Sure.
Uh my name's Pink Quang Lai.
I'm an assistant professor in the Department of Chemical Engineering and Material Science at Stevens Institute of Technology in New Jersey.
My research focuses on applying artificial intelligence, machine learning, molecular modeling, and biophysical characterization to accelerate antibody development.
In particular, my group studies antibody developability, properties such as viscosity, aggregation, stability, and subcutaneous availability, and how we can combine computational approaches with experimental techniques like small angle X-ray scattering, NMR, and molecular simulations to better understand and predict these behaviors.
Beyond my research, I'm also I also serve as an assistant editor for maps.
So it's been exciting to help shape discussions around emerging technologies in the antibody field.
Yeah, when we began planning this collection, AI was already transforming many areas of biology and drug discovery, but antibody development presents a unique set of challenges.
Unlike many other applications, antibody discovery generates highly diverse data from sequences and structures to developability measurements, formulation conditions, manufacturing data, and clinical outcomes.
The question was how we could bring these different data types together in meaningful and scientifically rigorous ways.
One of our primary goals was to create a forum that showcased not only new AI algorithms, but also practical applications across the entire anti-body development pipeline.
We wanted to highlight work ranging from antibodies discovery and engineering to developability prediction, formulation, manufacturing and quality assessment.
Another important objective was to encourage collaboration between computational scientists and experimental researchers.
The most impactful advances often come when machine learning is combined with mechanistic understanding and high quality experimental data, rather than being viewed as a replacement for experiments.
Finally, uh we wanted to the collection to serve as a resource for the community, a snapshot of where the field currently stands and where it's headed.
Yeah, I think we've certainly made a strong progress toward that those goals.
The level of community engagement has exceeded our expectations.
The number of submissions, the diversity of topics, and especially the readership
demonstrate that there is tremendous interest in this area.
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