Classification

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.

Tonifying traditional Chinese medicines occupy a central position in the traditional medical system, with their core value lying in the regulation of the body's functional state. Modern pharmacological studies have confirmed that these medicinal materials and their monomeric components possess multiple biological activities, including bidirectional immune regulation, anti-aging and lifespan extension, neuroprotection and cognitive enhancement, as well as hematopoietic and metabolic regulation. According to the traditional Chinese medicine theory of “strengthening the body’s resistance and consolidating the foundation”, tonifying medicines are mainly classified into four major categories: Qi-tonifying, Blood-tonifying, Yin-tonifying, and Yang-tonifying. This compound library strictly follows this classification system for compound collection.

Monomeric compounds derived from traditional Chinese medicines demonstrate excellent drug-like properties. They naturally possess structural diversity and clearly defined pharmacological activities, which help improve screening success rates and make them ideal tools for studying multi-target synergistic effects. This library contains 1,040 compounds, providing a material basis for investigating synergistic interactions among compounds (network pharmacology) and facilitating the development of multi-target therapeutic strategies for complex diseases such as cancer, neurodegenerative disorders, and metabolic syndrome.