Congbao Kang ,  Hung T. Nguyen ,  David E. Heppner ,  Bin Yu ,  Weijun Xu

Journal of Medicinal Chemistry

DOI: 10.1021/acs.jmedchem.6c00070

Over the past decade, RNA has emerged as an attractive and evolving landscape for small-molecule drug discovery. RNA functions as an intermediate macromolecule during gene expression and plays a diverse role in regulating cellular processes. (1) The promise in targeting RNA as therapeutic interventions arises from recent breakthroughs in structural biology, chemical biology, and computational modeling that collectively facilitate understanding of RNA biology. Multiple classes of RNA have been identified contributing to their functional diversity, including messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA (rRNA), long noncoding RNA (lncRNA), microRNA (miRNA), and other noncoding RNAs (ncRNAs). While only a tiny fraction of 1.5% of human genome is translated into proteins, approximately 75% is transcribed into RNA, (2−4) making RNA a vastly larger reservoir of potential drug targets than the traditional protein targets. Moreover, many disease-associated pathways including viral replication, cancer progression, neurodegeneration, and immune regulation are regulated by RNA or through its interactions with different proteins, making RNA an increasingly relevant and strategic focus for next-generation drug discovery. (5) Several classes of small molecules define the landscape of RNA-targeting drug discovery (Figure 1), and many other modulators of RNA have been discovered for further development. (6) Small molecules targeting RNA can act through diverse mechanisms, including direct RNA binding to modulate RNA function, disruption of RNA interactions with RNA binding proteins, and stimulation RNA binding to RNA degrading enzyme for degradation. (7) The success in developing RNA drugs and RNA ligands prove the feasibility of RNA-focused drug discovery. (2) Patients with spinal muscular atrophy (SMA) lack enough survival motor neuron (SMN) protein to maintain adequate muscle function. The lack of SMN protein is believed to drive the pathophysiology of SMA. In August 2020, the FDA approved risdiplam (Evrysdi, RG7916, RO7034067) developed by Roche for the treatment of SMA in patients of all ages. (8) As a small-molecule modulator of SMN2 splicing, risdiplam represents the first FDA-approved RNA-targeting small-molecule drug. Mechanically, risdiplam acts as a “glue”-like compound that enhances RNA binding protein (RBP) to RNA to facilitate splicing and increases the production of full-length functional SMN protein. In addition to risdiplam, there are other drugs or clinical candidates that directly bind to RNA, including small molecules targeting RNA G-quadruplexes such as Quarfloxin and CX-5461, ribosome-targeting antibiotics like macrolides, and viral RNA polymerase nucleosides like sofosbuvir. (9) Together, these precedences highlight the growing potential of RNA as a therapeutic target and lay the foundation for the rational discovery of small-molecule RNA modulators. Figure 1. Small-molecule drugs that target RNAs. While strong incentives have been directed to drug RNA, several fundamental challenges make RNA-targeted drug discovery inherently more intractable than conventional protein-directed approaches. (10) Although proteins can undergo complicated conformational changes and post-translational modifications, RNA is even more dynamic and flexible, adopting distinct conformations that depend on its sequence, cellular environment, and interactions with other biomolecules. (11) Said another way, the highly dynamic nature of RNA implies that robust pockets may be impossible to bind small molecules like unstructured regions of proteins. Unlike proteins formed by 20 amino acids, RNA is composed of only four nucleotides, presenting challenges for specific and tight interactions with small molecules. Additional hurdles include the limited identification of clearly druggable RNA motifs, the intrinsic structural dynamics of RNA, and the difficulty in achieving selectivity across closely related RNA sequences. Additionally, traditional protein-directed drug discovery often relies on both polar and nonpolar interactions within well-defined hydrophobic and hydrophilic pockets. By contrast, RNA features overwhelmingly negatively charged backbones and typically lacks hydrophobic pockets. As a result, the number and structural diversity of druggable RNA binding sites are expected to be limited, which further complicates efforts to achieve selective modulation. As such, the structural and functional nature of RNA intrinsically favors chemical space dominated by highly polar molecules manifesting to at least two major issues: limited selectivity due to the abundance of similarly polar features across cellular RNAs and significant challenges in optimizing ADME properties, particularly lipophilicity. As with drug discovery of protein targets, hit identification via high-throughput screening (HTS), fragment-based screening, and virtual screening are commonly adopted. A major challenge for HTS against RNA targets is that most existing HTS libraries were designed for proteins and are poorly suited for recognizing the unique physicochemical and structural characteristics of RNA. Therefore, the development of RNA-focused libraries has become a key priority, which requires pharmacophore-based strategies through incorporating scaffolds known to interact with RNA motifs. This can be further complemented by the rise of DNA-encoded libraries (DELs) that have emerged as powerful source for hit identification for RNA targets. (4) For RNA to become a more tractable and widely druggable class of biomolecules, advances are needed in generating well-behaved biochemical tools and obtaining detailed structural information on unique or transient RNA conformations. Such innovations will be essential for enabling robust discovery platforms analogous to those that have long supported successful drug development for proteins and enzymes. Despite the above-mentioned challenges, RNA-targeted therapeutics continue to gain momentum across oncology, virology, and neurology. Recent advances in structural biology and computation, artificial intelligence, and multiscale modeling have begun to overcome these barriers and pave a rational foundation for RNA-targeted drug discovery. Structural studies of RNA using chemical biology and biophysical methods have made progress in recent years, providing detailed insights into secondary and tertiary motifs such as hairpins, internal loops, bulges, and pseudoknots, which are considered important structural architectures for developing small molecules. NMR spectroscopy remains a powerful technique for probing RNA structure and dynamics in solution. (12) Its ability to resolve conformational equilibria, detect transient states, and characterize ligand binding has made it indispensable for understanding RNA functional motions and identifying druggable conformations. X-ray crystallography, usually hindered by the intrinsic flexibility of RNA, has become increasingly feasible due to improved construct design, presence of stabilizing ligands, and the use of RBPs to facilitate crystallization. These innovations have enabled high-resolution structures of diverse RNA motifs and RNA–ligand complexes to be solved. (13) Cryo-EM has transformed the field through efficient determination of large RNA molecules and RNA–protein complexes that were previously inaccessible. (14) Complementing experimental approaches, computational methods have evolved rapidly and played a critical role in RNA structural biology and drug discovery. (15) Several algorithms and servers have been developed to predict RNA secondary and tertiary structures, model conformational ensembles, binding pocket identification and screen compound libraries against RNA targets. (16) Machine learning based methods that are supported by continuously expanding high-quality structural data, have improved accuracy in predicting RNA structures, RNA–RBP interactions and RNA–ligand interactions. Collectively, these methods provide a robust foundation for rational discovery of RNA modulators. Advances in physics-based molecular dynamics (MD) simulations are now mapping RNA conformational landscapes to reveal hidden and metastable pockets. Enhanced sampling MD, (17) coarse-grained (CG) modeling, (18) and Markov state models (MSMs) (19) allow the identification of transient states that are invisible to standard spectroscopy and experimental determination. (20) Coupled with ensemble-based docking against multiple conformers, hit rates may improve compared to static docking against RNA targets. Furthermore, the integration of hybrid QM/MM and machine-learning (ML) corrected scoring functions is providing a more rigorous treatment of stacking interactions, hydration shells, and ion-mediated electrostatics, which are critical for accurate RNA-ligand affinity prediction. These methods are increasingly used to derive druggability maps for riboswitches, repeat RNAs, viral elements, and structured motifs within long non-coding RNAs. (21) On the other hand, AI-driven prediction and generative design for RNA-targeted chemistry is on the rise. However, a central barrier is the limited availability of high-resolution RNA–ligand structures, which constrains traditional structure-based design. Emerging AI approaches are addressing this gap through multimodal learning frameworks that integrate RNA sequence, chemical features, SHAPE/DMS reactivity, and evolutionary covariation. Deep learning models improve secondary and tertiary structure inference, enabling more accurate identification of ligandable motifs and conformational states relevant for binding. (22) Recently, prediction of small-molecule–RNA interactions was achieved without the need for RNA tertiary structures as input. (23) However, their broader utility in RNA-focused medicinal chemistry remains to be fully established and will likely require larger data sets and more rigorous experimental validation. Moreover, many disease-relevant RNAs function through multivalent interactions with RBPs. Small molecules that restore normal RNA–RBP equilibrium have demonstrated proof-of-principle activity in correcting splicing, transcriptional regulation, and translation. (24) It remains to be seen whether disrupting RNA–protein binding interaction would bring clinical benefits. As data sets from CLIP–seq, RNP–MaP, and ligand–RNA cross-linking expand, predictive modeling of RBP selectivity will continue to improve. Last but not least, a rapidly growing therapeutic direction targets ribonucleoprotein condensates, that feature dynamic assemblies whose physicochemical properties (viscoelasticity, aging kinetics, etc.) encode key regulatory functions. (25) Aberrant condensate behavior is implicated in many diseases including neurodegenerative diseases and cancer. (25) Small molecules that alter condensate properties, such as softening, hardening, dissolving, or preventing gelation, represent a new modality of RNA-focused therapeutics. (26) Computational modeling is expected to play a central role capturing the structures of mesoscale organization and emergent behaviors of ribonucleoprotein condensates. These approaches collectively support rational design of condensate-modulating drugs, a modality fundamentally different from classical lock-and-key pharmacology. Overall, deep understanding of RNA targets, innovative screening and validation as a collaborative effort from chemical biology and medicinal chemistry is key to the success of RNA targeted drug discovery campaigns. With rapidly advancing techniques in structural biology and artificial intelligence are being developed, we may witness whether small molecule drug discovery targeting RNAs will meet its inflection point or remain a niche curiosity in the years to come. This article references 26 other publications. (acccessed 2024/10/22) PubMed (acccessed 2026/01/04) (acccessed 2026/01/04) This article has not yet been cited by other publications.