AI and Computational Approaches in Antibody Research

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.

At Biointron, we are dedicated to accelerating your antibody discovery, optimization, and production needs. Our team of experts can provide customized solutions that meet your specific research needs. Contact us to learn more about our services and how we can help accelerate your research and drug development projects.

References:

1. 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

2. Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023). Computational and artificial intelligence-based methods for antibody development. Trends in Pharmacological Sciences, 44(3), 175–189. https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(22)00279-6