Prediction

A diverse compound library with favorable ADMET properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is crucial in drug discovery. Early evaluation of ADMET properties allows for the exclusion of molecules with unfavorable profiles at the initial stages, thereby reducing the risk of late-stage development failures, lowering R&D costs, and accelerating optimization of lead compounds. Based on predictions from ADMET-related AI algorithms, the compounds in this library are predicted to exhibit favorable oral bioavailability (F > 30%), reasonable plasma protein binding (PPB < 98%), minimized CYP3A4 inhibition potential (inhibition probability < 50%, CYP3A4 is the most critical drug-metabolizing enzyme in the cytochrome P450 family) , low toxicity profiles, with 140 potentially toxic substructures pre-identified and excluded via substructure searching to eliminate compounds containing hazardous fragments. The diversity library enables broad applicability in high-throughput screening (HTS) and high-content screening (HCS).

Drug-induced liver injury (DILI; also known as drug-induced hepatotoxicity) is caused by medications (prescription or OTC), herbal and dietary supplements (HDS), or other xenobiotics that result in abnormalities in liver tests or in hepatic dysfunction that cannot be explained by other causes. Drugs are an important cause of liver injury. Drug-induced hepatic injury is the most common reason cited for withdrawal of an approved drug.

DILI is thought to occur via several different mechanisms. Among these are direct impairment of the structural (e.g., mitochondrial dysfunction) and functional integrity of the liver; production of a metabolite that alters hepatocellular structure and function; production of a reactive drug metabolite that binds to hepatic proteins to produce new antigenic drug-protein adducts, which are targeted by hosts’ defenses (the hapten hypothesis); and initiation of a systemic hypersensitivity response (i.e., drug allergy) that damages the liver.

MCE Drug-induced Liver Injury (DILI) Compound Library contains a unique collection of 1,447 hepatotoxicity causing compounds and is a powerful tool to research DILI and other drug toxicities. This library can be used to understand the mechanisms of DILI, identify biomarkers for early DILI prediction, and allow timely recognition during drug development, thus finally achieving successful DILI prevention and assessment in the pre-marketing phase.

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.