Aptamers Meet Structural Bioinformatics, Computational Chemistry, and Artificial Intelligence

Abstract

Aptamers—short single-stranded DNA or RNA—are the latest biomolecules to fall within reach of powerful structure-prediction pipelines that blend bioinformatics, computational chemistry, and artificial intelligence. These tools now enable high-throughput exploration of aptamer conformational landscapes, a prerequisite for rational design and optimization of their exceptional target affinity and specificity. Next-generation sequencing has democratized library analysis, allowing any laboratory to handle millions of variants. Hybrid workflows currently offer the most reliable secondary and tertiary structure models, and explicit treatment of conformational flexibility is proving indispensable for mapping binding-competent states. Yet every predictive tier—from classic free-energy minimization to deep learning—still underrepresents chemically modified nucleotides, the very substitutions that grant therapeutic aptamers nuclease resistance and pharmacokinetic longevity. Capturing the structural and dynamical consequences of these modifications remains the key unsolved problem. Progress, therefore, hinges on two fronts: richer parameterization and training data that encompass modified bases, and tighter coupling of in silico screens with biophysical and structural validation. Bridging these gaps will convert the current wave of computational advances into clinically relevant aptamer-based drugs ready to be delivered to the patients.

This article is categorized under:

• Structure and Mechanism > Molecular Structures
• Data Science > Computer Algorithms and Programming
• Data Science > Artificial Intelligence/Machine Learning