octant rule

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.

19F-NMR has proved to be a detection mode in fragment-based drug discovery (FBDD) for studies of protein structure and interactions. 19F shows high sensitivity for NMR detection, and the exquisite sensitivity of 19F chemical shifts and linewidths to ligand binding all make it a valuable approach in FBDD.F (Fluorine) -Fragments can be used for 19F-NMR detection after binding to target proteins, and can be used as an effective 19F-NMR tool for FBDD.

MCE designs a unique collection of 5,067 F-fragments, all of which obey a heuristic rule called the “Rule of Three (RO3)”, in which molecular weight ≤300 Da, the number of hydrogen bond donors (H-donors) ≤3, the number of hydrogen bond acceptors (H-acceptors) is ≤3 and cLogP is ≤3. This F-fragments library is an important source of lead-like drugs.

Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function; it can cover broad swaths of chemical space and allows the use of creative chemistry. Fragment-based drug discovery is well-established in industry and has resulted in a variety of drugs entering clinical trials, with two, vemurafenib and venetoclax, already approved. FBDD also has key attractions for academia. Notably, it is able to tackle difficult or novel targets for which no chemical matter may be found in existing HTS collections.

MCE designs a unique collection of 41,368 fragment compounds, all of which obey a heuristic rule called the “Rule of Three (RO3) ”, in which molecular weight ≤300 Da, the number of hydrogen bond donors (H-donors) ≤3, the number of hydrogen bond acceptors (H-acceptors) is ≤3 and cLogP is ≤3. This library is an important source of lead-like drugs.

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 2,815 RO5 drug-like natural products, which is an important tool for drug discovery.

Fragment-based drug discovery (FBDD) offers a strategic advantage by categorizing fragment hits according to their functional groups. This approach facilitates both the further optimization of these hits and the rational design of larger compounds through fragment combination. The amine functional group plays a vital role in drug development, as evidenced by its presence in many marketed drugs like Galantamine, Tacrine, and Rivastigmine. It is instrumental in enhancing solubility, improving bioavailability, and ensuring shelf-life stability—all critical factors for drug efficacy.

MCE offers a collection of 20,086 amine fragments for drug discovery. All of these compounds adhere to the Rule of Three (RO3) criteria for drug-likeness, which MCE offers a collection of 20,086 amine fragments for drug discovery, all of which stipulates a molecular weight ≤ 300 Da, ≤ 3 hydrogen bond donors, ≤ 3 hydrogen bond acceptors, and a cLogP ≤ 3.

Covalent inhibitors are small molecules that can bind specifically to target proteins through covalent bonds and inhibit their biological functions. Although for a long time, covalent targeting has been playing a subordinate role in drug discovery, with an increasing number of reports on successful clinical applications of such drugs, the potential of these agents is now being acknowledged. Currently, cysteine is the most common covalent amino acid residue in a variety of covalent drugs, and various warheads have been developed that can react with cysteine, providing the key building blocks for covalent drugs to form covalent bonds.

To meet the development needs of covalent inhibitors targeting cysteine, MCE has designed a unique collection of 3,783 fragments with different covalent warheads that target cysteine. The MCE Cysteine Targeted Covalent Fragment Library is designed using the following covalent warheads: Acrylamides, Propiolic acid ester, Dimethylamine functionalized acrylamides, Chloroacetamides, Acrylonitrile, 2-Cyanoacrylamide, Aziridine, Haloacetamide, etc. All fragments are pre-filtered with the Rule of Three restrictions which can be used for fragment-based covalent drug development.

Molecular Glue Virtual Library is constructed using generative AI technology, integrating the structural features, activity data of known molecular glues, and interaction information of ternary complexes (target protein-E3-molecular glue). Endowed with structural novelty, drug-likeness, diversity and synthesizability, it is applicable to molecular glue-based AI drug screening and large-scale virtual screening.

MCE builds this library based on high-quality molecular building blocks by virtue of robust computing power, coupled with rigorous reaction rules and optimized compound generation strategies. To ensure library quality, molecules with high synthetic difficulty, poor drug-likeness, PAINS and other undesirable molecules are excluded first. Subsequently, scaffold-based compound analysis is performed to screen drug-like diverse molecules for synthesizability evaluation; those with excessively high synthetic difficulty are removed, ultimately forming a large-scale molecular glue virtual library with structural diversity, synthesizability and drug-likeness.

Compounds in the library can be synthesized in only 1-2 chemical reaction steps. With MCE’s experienced chemical synthesis team, custom synthesis of different scales from milligram to kilogram can be easily achieved to meet diverse customer needs.

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.

Protein protein interactions (PPI) have pivotal roles in life processes. The studies showed that aberrant PPI are associated with various diseases. However, the design of modulators targeting PPI still faces tremendous challenges, such the difficult PPI interfaces for the drug design, lack of ligands reference, lack of guidance rules for the PPI modulators development and high-resolution PPI proteins structures.

The PPI Library comprises molecules of various sizes, frameworks, and shapes ranging from fragment-like entities to macrocyclic derivatives designed as secondary structure mimetics or as epitope mimetics. The designs cover β-turn / loop mimetics and α-helix mimetics. Since helices present at the interface in 62% of all protein-protein interactions. This library focused on designs including mimics with the substitution geometry of an a-helices, as well as designs that mimic the location of “hot-spot” side chains in helix-mediated PPIs.