RNA Focused Library

The majority of clinically-approved small-molecule drugs target proteins. Meanwhile, it was shown that RNA is involved in progression of various diseases, including infectious diseases (virus infections, such as HIV, AIDS, hepatitis C), metabolic diseases (e.g., diabetes, cancer), and triplet repeat disorders (myotonic dystrophy, Huntington’s disease, etc.). Targeting RNAs with small-molecule drugs offers new opportunities to therapeutically modulate numerous cellular processes, directly regulated by them (such as transcription, splicing, translation, and epigenetic modifications), including those linked to 'undruggable' protein targets [1].

Recently, the number of established RNA-based molecular targets has grown, with a detailed elucidation of their structural and functional relationship being reported. Several small-molecule inhibitors have been successfully developed for a variety of RNA molecules [1-3]. Oncogenic microRNAs that are tightly involved in the development and progression of various cancers [3]. Novel RNA-based drug discovery strategies are being considered for the treatment of pan-drug resistant bacteria [4-5].

Life Chemicals has developed its RNA Screening Library of over 3,300 drug-like compounds with predicted RNA-binding activity as an excellent starting point for post-transcriptional gene regulation, antibacterial, and antiviral drug discovery research. It has been designed with two ligand-based approaches:

RNA Screening Subset by 2D Similarity Search

Initially, a set of about 750 reported molecules capable of binding to RNA has been collected from the literature (data collected by SMMRNA database) [1]. It was narrowed down by binding activity via discarding compounds with low dissociation constant (Kd > 10 μM). At the next step, a 2D similarity search has been performed against Life Chemicals HTS Compound Collection with Tanimoto and Tversky index values of ≥ 0.80 and ≥ 0.85, respectively. Fragment-Based/Chemical Hashed Fingerprints were used to compare compound structures. Finally, PAINS, reactive groups, and in-house developed medchem filters were applied to provide the resulting compound set.

RNA Screening Subset by Bayesian Modeling

The same training set of known RNA-binding compounds has been used to build a Bayesian model (Bayesian categorization methodology) to distinguish “good” molecules from the compounds with known activity. Both molecular fingerprints (circular FCFP6 fingerprints) and molecular properties (MW, No. of HBA, No. of HBD, LogP, PSA, No. of rotatable bonds, No. of rings) were involved in the construction of Bayesian model. At the next step, the model has been applied to Life Chemicals HTS Compound Collection in order to select screening compounds targeting RNA. PAINS, as well as those with "bad" and reactive groups, have been filtered out from the resulting compound set.

Compound selection by cherry-picking is available. Please contact us at orders@lifechemicals.com for price quotations and any specific requests.

Some representative compounds from Life Chemicals RNA Focused Library

Fig 1. Some representative compounds from Life Chemicals RNA Focused Library.


  1. Warner, K. D.; Hajdin, C. E.; Weeks, K. M. Principles for Targeting RNA with Drug-like Small Molecules. Nature Reviews Drug Discovery 2018, 17 (8), 547–558. https://doi.org/10.1038/nrd.2018.93.
  2. Mehta A, Sonam S, Gouri I, Loharch S, Sharma DK, Parkesh R. SMMRNA: a database of small molecule modulators of RNA. Nucleic Acids Res. 2014, 42 (Database issue): D132-41. https://doi.org/10.1093/nar/gkt976.
  3. Di Giorgio, A.; Duca, M. Synthetic Small-Molecule RNA Ligands: Future Prospects as Therapeutic Agents. MedChemComm. Royal Society of Chemistry August 14, 2019, pp 1242–1255. https://doi.org/10.1039/c9md00195f.
  4. Moellering RC. Linezolid: the first oxazolidinone antimicrobial. Ann. Intern. Med. 2003;138:135–142.https://doi.org/10.7326/0003-4819-138-2-200301210-00015.
  5. Parmeciano Di Noto, G.; Molina, M. C.; Quiroga, C. Insights Into Non-Coding RNAs as Novel Antimicrobial Drugs. Frontiers in Genetics 2019, 10 (FEB). https://doi.org/10.3389/fgene.2019.00057.