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Results for "

RNA,interaction

" in MedChemExpress (MCE) Product Catalog:

By Targets:	Apoptosis (1) DNA/RNA Synthesis (2) HuR (1)
By Research Areas:	Cancer (3) Inflammation/Immunology (1) Metabolic Disease (1) Neurological Disease (1)

Inhibitors & Agonists

Screening Libraries

Cat. No.	Product Name	Target	Research Areas	Chemical Structure

HY-119734

Gossypolone	DNA/RNA Synthesis	Cancer
Gossypolone is an RNA-binding protein Musashi-1 inhibitor with a K_i of 12 nM. Gossypolone disrupts the Musashi-numb RNA interaction and directly binds to the RBD1 of MSI1 protein. Gossypolone can be used for the research of cancer .

HY-139059

ERD03	Others	Neurological Disease
ERD03 is a potent disruptor of the EXOSC3-RNA interaction, with a K_d of 17±7 μM . ERD03 induces PCH1B-like phenotype in zebrafish embryo and can be used for neurological disorder disease research .

HY-124844

Azaphilone-9 AZA-9	HuR	Cancer
Azaphilone-9 (AZA-9) is an inhibitor of HuR-ARE RNA interaction (IC₅₀=1.2 μM) by binding RNA-binding protein Hu antigen R (HuR). The HuR-RNA interactions stabilize many oncogenic mRNAs in tumors. Thus Azaphilone-9 potentially inhibit cancer cell growth and progression .

HY-164910

AHL-Pu-2	DNA/RNA Synthesis	Metabolic Disease Inflammation/Immunology
AHL-Pu-2 is the clickable electrophilic purine. AHL-Pu-2 can be used to directly quantify protein-RNA interactions on proteins through photoaffinity competition with 4-thiouridine (4SU)-labeled RNA in cells .

HY-W240322

PSF-IN-1	Apoptosis	Cancer
PSF-IN-1 (Compound No.10-3) is a RNA-binding protein (PSF) inhibitor and can effectively inhibit the PSF-RNA interaction (IC₅₀: 2.2 pM). PSF-IN-1 can increase histone acetylation, inhibit tumor cell proliferation and induce apoptosis. PSF-IN-1 has antitumor activity .

Cat. No.	Product Name

HY-L917

RNA Binding Library	5,000 compounds
RNA is crucial for the regulation of numerous cellular processes and functions. With the in-depth study of disease mechanisms, processes such as RNA expression, splicing, translation, and stability regulation have become new targets for disease intervention. RNA has provided new therapeutic modalities for metabolic diseases, genetic disorders, and cancer patients, resulting in several innovative drugs. MCE R&D team collected small molecules targeting RNA from the PDB, R-BIND, ROBIN, and internal database as the positive dataset, and non-targeting RNA small molecules from ROBIN as the negative dataset. Based on the GeminiMol pre-trained model, we encoded the molecules and calculated over 1700 molecular descriptors using Mordred as inputs for the model. Subsequently, we employed 13 deep learning models to learn from the data. All of which yielded good training results, with AUROCs greater than 0.75. Ultimately, we selected the Finetune model to screen HY-L901P, which exhibited the best classification performance, achieving an AUROC of 0.82 and a prediction accuracy of 0.76. We then applied filtering based on StaR rules (with at least two of the following properties: cLogP ≥ 1.5, Molar Refractivity ≥ 4, Relative Polar Surface Area ≤ 0.3) to obtain a library containing approximately 5,000 small molecule compounds targeting RNA. This library serves as a valuable tool for screening small molecules that interact with RNA.

RNA Binding Library

5,000 compounds

RNA is crucial for the regulation of numerous cellular processes and functions. With the in-depth study of disease mechanisms, processes such as RNA expression, splicing, translation, and stability regulation have become new targets for disease intervention. RNA has provided new therapeutic modalities for metabolic diseases, genetic disorders, and cancer patients, resulting in several innovative drugs.

MCE R&D team collected small molecules targeting RNA from the PDB, R-BIND, ROBIN, and internal database as the positive dataset, and non-targeting RNA small molecules from ROBIN as the negative dataset. Based on the GeminiMol pre-trained model, we encoded the molecules and calculated over 1700 molecular descriptors using Mordred as inputs for the model. Subsequently, we employed 13 deep learning models to learn from the data. All of which yielded good training results, with AUROCs greater than 0.75. Ultimately, we selected the Finetune model to screen HY-L901P, which exhibited the best classification performance, achieving an AUROC of 0.82 and a prediction accuracy of 0.76. We then applied filtering based on StaR rules (with at least two of the following properties: cLogP ≥ 1.5, Molar Refractivity ≥ 4, Relative Polar Surface Area ≤ 0.3) to obtain a library containing approximately 5,000 small molecule compounds targeting RNA. This library serves as a valuable tool for screening small molecules that interact with RNA.

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