Evaluation of single-template ligand-based methods for the discovery of small-molecule nucleic acid binders

Nucleic acid molecules, including ribonucleic acid (RNA) and deoxyribonucleic acid (DNA), are essential for various biological processes and can adopt diverse 3D structures that serve as potential drug targets. The direct targeting of nucleic acid structures by small molecules represents an emerging field in drug design with enormous potential for providing therapeutic options for diseases that are currently not addressed, including genetic diseases and viral infections. In the early days of this promising field, a shortage of reliable structural data presents a bottleneck to the direct adaptation of structure-based methods, making the simpler yet powerful ligand-based approach an attractive alternative for virtual screening. In this study, we thoroughly evaluate and benchmark these methods against the reported binding of small molecules to diverse nucleic acid targets. We also compare these methods with structure-based molecular docking. Our results demonstrate that classification performance is significantly influenced by the applied descriptors, the chosen similarity measure, and the specific nucleic acid target. We have also proposed a consensus method that combines the best-performing algorithms of distinct nature. According to our studies, this approach outperforms all other tested methods, providing a valuable framework for nucleic acid-targeted drug discovery.