In recent years, structured RNAs have become tractable drug targets, enabling the discovery of selective modulators for riboswitches, long noncoding RNAs, G-quadruplexes, and RNA–protein complexes.
Breakthrough advances in library design, screening methodologies, and AI-based prioritization have expanded the scope of RNA-targeted drug discovery (Kovachka et al., Nat Rev Chem 2024; Lundquist et al., SLAS Discovery 2025; Morishita, ChemMedChem 2022; Momentum Bio, 2024).
Modern RNA-focused screening now combines:

• Affinity Selection–Mass Spectrometry (AS-MS) for label-free, solution-phase discovery,

• SPR/BLI for kinetic profiling and specificity ranking,

• AI-guided virtual enrichment to prioritize compounds with high “RNA-likeness” before experimental testing (Graff et al., Chem Sci 2020; Cao et al., Nat Mach Intell 2023).

OTAVA’s RNA-Binding Library (AI-Optimized) is a focused collection of 715 compounds designed to probe small-molecule interactions with structured RNA motifs.

The set integrates AI-driven cheminformatics, physicochemical optimization, and scaffold-level clustering to ensure a balanced representation of RNA-privileged chemotypes suitable for high-throughput screening.

Library Design and Optimization

The AI-optimization workflow was applied to OTAVA’s in-stock collection to generate an enriched RNA-targeted subset:

• Physicochemical filtering

• pKa-balance filtering

• PAINS/REOS exclusion

• Murcko scaffold clustering (Tanimoto ≥ 0.75) using Butina algorithm.

• AI-based desirability scoring (RepScore) combining TPSA, LogP, flexibility, and QED.

Result: 715 representative compounds covering unique Murcko scaffolds, ensuring broad yet nonredundant coverage of RNA-relevant chemical space.

The AI-optimized core of 715 representative compounds can be readily expanded to approximately 1,030 structures by including all pre-clustered analogs prior to Murcko filtering, and further extended to about 2,300 predicted candidates generated through Bayesian modeling–guided enrichment of RNA-privileged chemotypes.

Scaffold Diversity Overview

The AI-filtered RNA library retains a structurally rich distribution of scaffolds typical for RNA-binding chemotypes.

Most frameworks contain 9–25 analogs each, with an average molecular weight of ~340 Da and cLogP 2.9–4.5 — parameters consistent with experimentally validated RNA ligands.

Chemospace Coverage

Principal-component analysis (PCA) of ECFP4 fingerprints confirms even and continuous coverage of the RNA-relevant chemical space.
The library features one dense, drug-like core (TPSA 60–90, cLogP 1.5–4.5) and smaller peripheral clusters representing alternative heteroaromatic scaffolds.
This ensures both exploratory breadth and focused screening efficiency.

AI-Accelerated Mapping of RNA-Targeted Small-Molecule Space: Library Design, Multi-Modal Screening, and Synthesis-Aware Optimization Review by OTAVA

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