AI-accelerated therapeutic antibody development: practical insights
Antibodies represent the largest class of biotherapeutics thanks to their high target specificity, binding affinity and versatility. Recent breakthroughs in Artificial Intelligence (AI) have enabled information-rich in silico representations of antibodies, accurate prediction of antibody structure f...
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| Main Authors: | , , , |
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| Format: | Book |
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Frontiers Media S.A.,
2024-09-01T00:00:00Z.
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|---|---|---|---|
| 001 | doaj_a0b6669d453d4d6fb2f9ee963c3a01ee | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Luca Santuari |e author |
| 700 | 1 | 0 | |a Marianne Bachmann Salvy |e author |
| 700 | 1 | 0 | |a Ioannis Xenarios |e author |
| 700 | 1 | 0 | |a Ioannis Xenarios |e author |
| 700 | 1 | 0 | |a Bulak Arpat |e author |
| 245 | 0 | 0 | |a AI-accelerated therapeutic antibody development: practical insights |
| 260 | |b Frontiers Media S.A., |c 2024-09-01T00:00:00Z. | ||
| 500 | |a 2674-0338 | ||
| 500 | |a 10.3389/fddsv.2024.1447867 | ||
| 520 | |a Antibodies represent the largest class of biotherapeutics thanks to their high target specificity, binding affinity and versatility. Recent breakthroughs in Artificial Intelligence (AI) have enabled information-rich in silico representations of antibodies, accurate prediction of antibody structure from sequence, and the generation of novel antibodies tailored to specific characteristics to optimize for developability properties. Here we summarize state-of-the-art methods for antibody analysis. This valuable resource will serve as a reference for the application of AI methods to the analysis of antibody sequencing datasets. | ||
| 546 | |a EN | ||
| 690 | |a LLM | ||
| 690 | |a ALM (antibody language model) | ||
| 690 | |a developability | ||
| 690 | |a inverse folding | ||
| 690 | |a deep learning | ||
| 690 | |a artificial intelligence | ||
| 690 | |a Therapeutics. Pharmacology | ||
| 690 | |a RM1-950 | ||
| 655 | 7 | |a article |2 local | |
| 786 | 0 | |n Frontiers in Drug Discovery, Vol 4 (2024) | |
| 787 | 0 | |n https://www.frontiersin.org/articles/10.3389/fddsv.2024.1447867/full | |
| 787 | 0 | |n https://doaj.org/toc/2674-0338 | |
| 856 | 4 | 1 | |u https://doaj.org/article/a0b6669d453d4d6fb2f9ee963c3a01ee |z Connect to this object online. |