What is RNA?
RNA is often thought of as an unstructured linear biomolecule, but it is in fact structurally rich and capable of forming complex 3D architectures such as aptamers, riboswitches, hairpins, and G-Quadruplexes. Whilst even structured RNAs are highly dynamic, they can create well defined ligand binding pockets suitable for selective small molecule modulation.
Clinically validated RNA-binding small molecules are establishing RNA as a powerful new class of drug targets:
- Ribocil - translational inhibitor binding an FMN riboswitch
- Risdiplam - FDA approved splicing modulator targeting SMN2 pre-mRNA
- Zotatifin - translation regulator stabilising eIF4A-RNA complexes
Domainex’s Integrated Strategy for Discovering RNA-Binding Small Molecules
Domainex delivers a collaborative, rapid, and highly flexible RNA-focused discovery workflow that combines:
- RNA-focused multi-objective virtual screening via Pareto front selection
- High‑sensitivity biophysical characterisation using Spectral Shift and Grating-Coupled Interferometry (GCI)
This integrated approach, powered by Domainex’s structural and biophysical expertise, enables confident identification, validation, and mechanistic profiling of RNA-ligand interactions.
Computational Virtual Screening for RNA‑Ligand Discovery
Ligand-Based Drug Design (LBDD)
Domainex's ligand-based models rapidly triage chemical space to identify fragments and compounds resembling known RNA-binding chemotypes. These approaches leverage key molecular descriptors including:
- Shape complementarity
- Electrostatic similarity
- Pharmacophore similarity
- Physiochemical properties
This enables rapid exploration of RNA-relevant chemical space and prioritisation of compounds with the highest likelihood of productive RNA interactions.
Structure-Based Drug Design (SBDD)
To complement ligand-based approaches, Domainex deploy advanced structure-based workflows specifically tailored to RNA targets. These incorporate:
- RNA pocket detection algorithms
- State-of-the-art RNA-ligand docking
- AI-driven RNA binding probability models
These approaches can identify and characterise druggable binding sites.
Our methods can account for the unique structural features of RNA, including:
- Conformational flexibility
- Non-canonical base pairing
- Distinct electrostatic environments
Together, these approaches provide detailed structural insight to guide RNA-targeted ligand discovery and optimisation.
Hybrid AI‑Enhanced Virtual Screening
Domainex integrates LBDD and SBDD into a unified, AI‑supported pipeline that:
- Predicts ligand complementarity with high accuracy
- Prioritises selective vs non‑selective binders
- Rapidly ranks our proprietary fragment library for RNA-focused hit discovery
This hybrid approach offers speed, adaptability, and high enrichment, enabling rapid project progression.