therapeutic resistance

Targeted protein degradation(TPD) is a novel and promising approach to new drug discovery and development. It shows great potential for treating diseases with “undruggable” pathogenic protein targets and for overcoming drug resistance. Molecular glues and PROTACs are both targeted protein degraders that have attracted the most attention.

Molecular glues are small molecular degraders that mainly induce novel interaction between an E3 ligase and a target protein to form a ternary complex, leading to protein ubiquitination and subsequent proteasome degradation. Compared with PROTACs, molecular glues generally possess more favorable drug-like properties, such as lower MW, higher cell permeability, and better oral absorption. Molecular glues are emerging as a promising new therapeutic strategy.

MCE supplies a unique collection of 105 molecular glues which target various proteins. MCE Molecular Glue Compound Library is a useful tool to conduct scientific research and disease mechanism study.

Ovarian cancer is the most common cause of death in female genital malignancies, with the highest mortality rate in female genital malignancies. It is characterized by difficulty in detection in the early stage of the disease, high recurrence rate and poor prognosis. In fact, ovarian cancer includes many pathologic types. It is usually divided into epithelial ovarian cancer, malignant germ cell tumors and sex cord stromal tumors, of which epithelial ovarian cancer is the most dominant form. Clinical treatment of ovarian cancer prioritizes surgery combined with paclitaxel chemotherapy. However, due to the spread and drug resistance of tumor cells, the recurrence of ovarian cancer is high. In this case, combined with traditional methods, the development of new therapeutic agents can help to improve the treatment effect of ovarian cancer.

MCE designs a unique collection of 2,817 compounds with definite or potential anti-ovarian cancer activity, which mainly targeting the main targets of ovarian cancer such as PARP, ATM/ATR, VEGFR and HIF/HIF Prolyl-Hydroxylase, etc. It is an essential tool for development and research of anti-ovarian compounds.

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 1,036 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.

Macrocyclic compounds (≥12-atom cyclic small molecules/peptides) have unique physicochemical properties. They form preorganized conformations with high binding affinity/selectivity, target traditional small-molecule-inaccessible proteins, and bridge small-molecule drugs and biological agents. As key protein phosphorylation enzymes, kinases are linked to tumors, COPD, etc., and are critical therapeutic targets. Traditional small-molecule kinase inhibitors lack selectivity, causing off-target toxicity, low bioavailability, and acquired resistance. Macrocycles’ semi-rigid structure restricts conformations, boosts binding selectivity, optimizes pharmacokinetics, and makes macrocyclization a core kinase inhibitor optimization strategy.

Thousands of bioactive macrocycles were curated from ChEMBL. Via Transformer, macrocyclization was converted into a chemical language translation task, enabling end-to-end macrocycle generation from linear precursors with simplified inputs. Macformer achieves efficient, automated linear molecule macrocyclization via deep learning; generated macrocycles have diversity, novelty, biocompatibility, and cover broader chemical space.

MCE collected thousands of marketed/clinical kinase inhibitors, using their fragments for macrocyclization to generate derivatives. After evaluating synthetic accessibility and physicochemical properties, a million-scale virtual macrocyclic library was built for kinase-related virtual and AI-driven screening.

Macrocyclic compounds (≥12-atom cyclic small molecules/peptides) have unique physicochemical properties. They form preorganized conformations with high binding affinity/selectivity, target traditional small-molecule-inaccessible proteins, and bridge small-molecule drugs and biological agents. As key protein phosphorylation enzymes, kinases are linked to tumors, COPD, etc., and are critical therapeutic targets. Traditional small-molecule kinase inhibitors lack selectivity, causing off-target toxicity, low bioavailability, and acquired resistance. Macrocycles’ semi-rigid structure restricts conformations, boosts binding selectivity, optimizes pharmacokinetics, and makes macrocyclization a core kinase inhibitor optimization strategy.

Thousands of bioactive macrocycles were curated from ChEMBL. Via Transformer, macrocyclization was converted into a chemical language translation task, enabling end-to-end macrocycle generation from linear precursors with simplified inputs. Macformer achieves efficient, automated linear molecule macrocyclization via deep learning; generated macrocycles have diversity, novelty, biocompatibility, and cover broader chemical space.

MCE collected thousands of marketed/clinical kinase inhibitors, using their fragments for macrocyclization to generate derivatives. After evaluating synthetic accessibility and physicochemical properties, a million-scale virtual macrocyclic library was built for kinase-related virtual and AI-driven screening.