Chemoinformatic Clustered Compound Library

Our Chemoinformatic Clustered Compound Library (CCC Library) represents a cutting-edge collection of compounds carefully curated to support high-throughput screening and medicinal chemistry investigations. Built upon the Bemis-Murcko scaffolding approach, it offers unique and diverse core structures, facilitating the exploration of chemical space with unparalleled diversity and specificity. By decomposing complex molecules into their fundamental scaffolds, we have obtained a screening set of over 75,000 screening compounds.

The Chemoinformatic Clustered Compound Library is a foundational tool for structure-based drug design (SBDD) to allow the systematic evaluation of scaffold-activity relationships. Such libraries generally enhance the efficiency of screening campaigns and forecast the rational development of next-generation therapeutics, advancing the frontiers of chemical and pharmaceutical sciences.

The compound selection can be customized based on your requirements. Cherry-picking is available.

Please, contact us at orders@lifechemicals.com for any additional information and price quotations.

Some related products not to be missed:

• Scaffolds and Scaffold-based Compounds
• Phenotypic Screening Libraries
• Biologically Active Compound Library
• Custom Synthesis Services

Compound selection

Compounds were selected from the HTS Compound Collection and filtered with substructure filters to exclude undesirable compounds such as PAINS, REOS, and reactive-compliant molecules. Subsequent filtration on physicochemical parameters ensured the selection of only viable screening compounds. Then, the Bemis-Murcko scaffolding algorithm was applied to the filtered collection to create clusters of molecules. Each cluster corresponds to a variety of molecules per the corresponding Bemis-Murcko Scaffold (Fig. 1).

Chemoinformatic Clustered Compound Library enables researchers to identify and exploit novel bioactive frameworks, accelerating the discovery of therapeutic agents with optimal pharmacological profiles (Fig. 2-3).

Butina clustering algorithm was used with Morgan Fingerprints to select the most diverse screening compounds for each scaffold. The number of molecules chosen was proportional to the general number of compounds for a particular cluster (Fig. 2).

Figure 1. Mechanism of library building using Bemis-Murcko scaffolding (BMS).

Figure 2. General hexagonal bin plot visualization for chemical space of the Chemoinformatics Clustered Compound Library using the UMAP dimensionality reduction method. Color intensity represents the number of molecules each hex includes [1].

Figure 3. Lipinski's and FSP³ descriptors calculated for the Chemoinformatic Clustered Compound Library using RDKit.

References:

1. Cihan Sorkun, M., Mullaj, D., Koelman, J., & Er, S. (2022). ChemPlot, a Python Library for Chemical Space Visualization. Chemistry–Methods, 2(7), e202200005.