library screen

MCE Novel Bioactive Compound Library consists of 3,091 bioactive compounds with validated bioactivities tested by cell-based assays or biochemical assays. All compounds in this library are structurally novel and bioactivity diverse which makes it easier to discover new lead compounds. MCE Novel Bioactive Compound Library, as a supplement of MCE bioactive compound library (HY-L001), is a useful tool to screen new lead compounds.

MCE 5K Scaffold Library consists of 5,000 lead-like compounds. Each compound represents one unique scaffold. All compounds are compatible with Lipinski’s rule (Rule of 5) with multiple characteristics such as calculated good solubility (-3.2 < logP < 5), oral bioavailability (RotB <= 10), drug transportability (PSA < 120). Compounds contained within the library have been screened to remove any inappropriate chemical structures, avoiding “false hits”. The sufficient diverse of compound structure makes this library a powerful tool for drug screening.

Mitochondrial autophagy refers to the selective encapsulation and degradation of damaged mitochondria by cells through the autophagy mechanism, thereby maintaining mitochondrial and cellular homeostasis. The concept of mitochondrial autophagy has received extensive attention since it was proposed. Current studies have shown that the mechanisms of mitochondrial autophagy can generally be divided into two categories: Ubiquitin-dependent pathways and Ub-independent pathways. In addition, mitochondrial autophagy is a research hotspot related to the pathogenesis of neurodegenerative diseases, cardiovascular diseases, cancer, metabolic diseases and other clinical diseases. Therefore, high-throughput screening based on mitochondrial autophagy can effectively screen out compounds that are closely related to the occurrence of diseases and analyze their mechanisms.

MCE can provide a library of 651 mitophagy compounds, which can be used for drug development and mechanism research in cancer, immunity, infection and other hot research fields.

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.

Natural products are small molecular compounds that occur in nature and come from any organism, including primary and secondary metabolites. Natural products have potential biological activity and can be used as lead compounds for drug discovery. Nature has been a source of medicines for thousands of years, and a large number of drugs have been isolated from nature, many based on their use in traditional medicine. With the development of compound targets, there is an increasing need to screen for compound diversity. Through ongoing research into natural biodiversity, much of which remains to be exploited, natural products will play a key role in meeting this demand. The Lipinski rule of 5 is used to describe the drug-like properties of a molecule, molecules that adhere to the rule of 5 have higher drug potential. Based on MCE natural product library, MCE selects the molecules that obey the rule of 5, which makes the efficiency of drug screening higher.

MCE designs a unique collection of 2,712 RO5 drug-like natural products, which is an important tool for drug discovery.