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AI and Computational Approaches in Antibody Research
Biointron2024-01-24Read time: 1 min
Image credit: DOI: 10.3390/ijerph18010271
Artificial intelligence (AI) and computational methods are a powerful tool for antibody discovery and engineering. Despite historical prevalence in small molecule-related applications, AI is progressively being utilized in the discovery and advancement of therapeutic antibodies, backed by the surge in computational power and innovative algorithms, with an increasing amount of data obtained through next-generation sequencing and related drug modalities such as VHH antibodies.1
The main uses of these approaches in antibody research involve2:
Building antibody databases
Target discovery and validation
Structural and functional modeling
Development assessment and activity improvements
De novo antibody design
In combination with other tools (e.g. bioinformatics and X-ray crystallography protein structure analysis)
These computational methods can provide much more cost-efficient and rapid turnaround time compared to the laborious experimental methods that are common in antibody discovery. Escalating costs are often a significant impediment to the advancement of drug discovery.
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References:
Norman, R. A., Ambrosetti, F., Bonvin, A. M., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020). Computational approaches to therapeutic antibody design: Established methods and emerging trends. Briefings in Bioinformatics, 21(5), 1549-1567. https://academic.oup.com/bib/article/21/5/1549/5581643
