Comprehensive evaluation of artificial intelligence-empowered approaches for protein-aptamer complex prediction

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported in abstract

• Primary Outcome: Comparative performance of AI frameworks in protein-aptamer complex prediction

• Nnt Or Sensitivity: Structural accuracy and binding affinity prediction metrics not quantified in abstract

• Confidence Interval: Not reported

Clinical Application

High feasibility as computational methods require only standard bioinformatics infrastructure Relevant to Australian biotechnology sector and academic research institutions developing novel therapeutics. Could inform TGA regulatory science approaches for aptamer drug evaluation Researchers and pharmaceutical companies developing aptamer-based therapeutics

Abstract

Drug discovery is a time-consuming, expensive, and high-risk process. Recent advances in artificial intelligence (AI) have enabled major breakthroughs in small-molecule and protein therapeutics. However, AI-driven design of aptamer drugs remains largely unexplored. Aptamers are short (15-100 nt) single-stranded DNAs or RNAs that exhibit high binding affinity, high specificity, and low immunogenicity, making them promising candidates for disease (such as cancer) therapeutics. Compared with protein-ligand or protein-protein systems, protein-aptamer complexes are under-represented in public structural databases, and aptamers themselves are highly flexible and relatively large molecules. These characteristics present distinct challenges for AI-based structural modeling. Here, we systematically evaluate recent AI frameworks, including AlphaFold3, Chai-1, Boltz-2, and RoseTTAFold2NA, along with a template-based approach, in predicting protein-aptamer complex structures and estimating binding free energies. We establish an independent benchmark to assess their performance in structural accuracy, stability, and energetic consistency. This study provides a foundation for the application of AI in aptamer drug design and offers a reference framework for future research in nucleic-acid therapeutics and biomolecular modeling.

References

1. 1.Zhao, J., Tram, K., Yan, H., & Li, Y. (2026). Comprehensive evaluation of artificial intelligence-empowered approaches for protein-aptamer complex prediction. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbag206