The success of high throughput screening in finding decent starting points for drug discovery heavily depends on the quality of the compound library. Compound screening libraries nowadays must bear an essential degree of novelty, possess contemporary lead-like properties and be built on “live” chemistry that can supply related chemical entities.

Only within last three years Enamine synthesized over 790 000 screening compounds for its public catalog. These substances feature a new structural diversity of Enamine unique building blocks and scaffolds. Having the largest commercial screening collection in the world, Enamine remains the only library producer that keeps enhancing its collection with significant number of new compounds each year. The renowned quality of compounds has been distilled in the design of two Discovery Diversity Sets (DDS). Discovery Diversity Sets are based only on the lead-like compounds including representatives of all three screening collections of Enamine – HTS, Advanced, and Premium.

The Discovery Diversity Sets are highly recommended for random screening against new as well as popular targets because of diversity of scaffolds and presence of latest building blocks.

Discovery Diversity Sets have been plated at 10 mM concentration in DMSO and can be swiftly supplied in different customized formats including following most popular options:

The hits discovered from Discovery Diversity Sets are expected to easily yield lead compounds. All identified hits can be readily followed with analogues either from stock or from new syntheses through our REAL database technology. All building blocks for lead optimization programs are readily available from Enamine stock.

Key features

• Novel compounds
• No PAINS, only medchem friendly compounds
• Express follow-up guaranteed

Comparative analysis of DDSs and commercially available compounds

74% of the compounds from DDS 10K are available exclusively at Enamine

Examples of the molecules

Screening of natural products has inspired discovery of remarkable number of drugs. Enrichment of small libraries with compounds possessing structural motifs validated by nature allows reaching better molecular profile and improves biological response. A special place in drug development should belong to Natural Product-like Fragments as promising starting points within attractive chemicals space.

Key features

• Scaffold Tree approach was applied to the Universal Natural Product Database (UNPD) comprising over 200K compounds to extract initial Scaffold Set.
• MedChem structural filters and refinements were used in order to remove trivial chemotypes, PAINS and overpopulated cores resulting in the Reference Scaffold Set with 550 structures.
• Substructure and similarity searches were applied to extract compounds bearing natural-like cores and moieties. Scaffold frequency analysiswas carried out to finalize and optimize fragment library.

Examples of the molecules

Vindoline

Z2143576554

Z1289582915

Ajmaline

Z2865591256

Z274799154

Z1255404894

Omacetaxine mepesuccinate

Z1494787051

Z754108110

Protein-protein interactions regulate most aspects of life cycle thereof being the most attractive and perspective target for contemporary drug development. This field is still not well explored and there are no common rules for proteins involved in this type of interaction. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods and algorithms. However, fragment-based approach showed fruitful results in search of new PPI inhibitors. We created new library of PPI fragments with dedicated design that consists of systematical knowledge of common PPI inhibitors and selection by privileged structural motifs.

Using our PPI Fragments for hit finding you receive multiple benefits allowing you to save on time and costs in lead generation. Each fragment from the library can be easily followed with available in stock analogues or synthesis of new derivatives through our REAL Database technology.

Key features

Recent researches in the field of PPI fragments revealed the requirement of higher molecular weight molecules compared to common fragments due to a larger contact surface area. Therefore “Rule of four” restriction were used to extract initial PPI fragments subset, as they are typically larger and more lipophilic. Additionally, machine-learning method (decision tree) with number of validated descriptors was used to refine the library.

The “hot-spots” concept has been applied, implying the use of “key” amino acid residues involved in PPIs. A significant number of the selected fragments contains groups/moieties which correspond to these hot-spots. Additionally, library was enriched with the molecules having alpha helix-like structure, able to mimic protein motifs. Also since hydrogen bonds often play a crucial role in PPIs the preference was set for the molecules bearing at least one H-bond donor (>70%) and one H-bond acceptor (100%).

Examples of the molecules

Z2010009926

Z1728138994

Z1446845287

Z1436203819

Z1892408066

Z1634931513

Z991146812

Z1868463771