model molecule

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.

Ion channels are key proteins on the cell membrane that regulate the flow of ions across membranes. They participate in nearly all physiological processes, including nerve conduction, muscle contraction, heart rhythm, and pain perception. Abnormalities in their function can lead to various serious diseases such as arrhythmia, epilepsy, hypertension, neuropathic pain, and cancer. Therefore, ion channels are highly valuable drug targets—over 15% of approved drugs target ion channels currently, demonstrating their irreplaceable therapeutic value in cardiovascular, neurological, and analgesic fields.

MCE has collected a library of over 5,000 reported ion channel-related bioactive compounds targeting major sites such as Na+ channels, K+ channels, Ca2+ channels, GABA receptors, iGluRs, and others. Using AI models, these compounds are characterized through both 2D representations (molecular fingerprints, pharmacophores) and 3D representations (3D conformation) to screen for a collection of lead-like compounds highly similar to known active molecules. Additionally, an hERG channel prediction algorithm integrating XGB and ISE mapping strategy is employed to assess and exclude potential cardiotoxicity in the library.. This step significantly reduces safety risks in subsequent screenings, particularly for ion channel drug development related to cardiovascular systems (e.g., Nav1.5, Cav1.2), effectively minimizing failures due to hERG inhibition and serving as a valuable tool for ion channel drug screening.

Techniques for reprogramming somatic cells create new opportunities for drug screening, disease modeling, artificial organ development, and cell therapy. The development of reprogramming techniques has grown exponentially since Yamanaka reprogrammed somatic cells to become induced pluripotent stem cells (iPSCs) using four transcription factors, OCT4, SOX2, KLF4, and c-MYC in 2006. Despite the development of efficient reprogramming methods, most methods are inappropriate for clinical applications because they carry the risk of integrating exogenous genetic factors or use oncogenes. Alternative approaches, such as those based on miRNA, non-viral genes, non-integrative vectors, and small molecules, have been studied as possible solutions to the problems. Among these alternatives, small molecules are attractive options for clinical applications. Reprogramming using small molecules is inexpensive and easy to control in a concentration- and time-dependent manner. It offers a high level of cell permeability, ease of synthesis and standardization, and it is appropriate for mass-producing cells.

MCE Reprogramming Compound Library contains a unique collection of 2,567 compounds that act on reprogramming signaling pathways. These compounds are potential stimulators for reprogramming. This library is a useful tool for researching reprogramming and regenerative medicine.