RNA binding

The RNA-targeted bioactive compound library is a high-quality collection of small molecules specifically designed and curated to target RNA structures and functions. It is widely applied in cutting-edge drug discovery and life science research. Unlike traditional strategies that focus on protein targets, RNA-targeted compounds can directly modulate various functional RNA molecules by influencing their splicing, translation, stability, or structural conformation, thereby enabling precise intervention in key biological processes. In the field of drug development, these compounds provide a novel approach to addressing previously “undruggable” targets and have demonstrated significant potential in areas such as oncology, antiviral therapies, and neurodegenerative diseases. For example, by targeting disease-associated RNA structural domains or regulating the aberrant expression of non-coding RNAs, these compounds can effectively inhibit disease progression or restore normal cellular function. In mechanistic studies, RNA-targeted compounds serve as valuable chemical biology tools to elucidate the roles of RNA in gene expression regulation, cellular signaling pathways, and disease development.

The MCE RNA-targeted bioactive compound library contains 858 compounds, sourced from databases such as TargetRX Atlas and R-BIND. The library features excellent structural diversity and biological activity, making it suitable for high-throughput screening (HTS), target validation, phenotypic screening, and lead compound discovery. It represents a valuable resource for RNA-related research and innovative drug development.

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.