Building Blocks Catalog

300 Thousand compounds in stock

Original and unique

Make-on-demand
Building Blocks

1B novel building blocks

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Custom Synthesis

Over 650 highly skillful chemists

Unique synthesis technologies

Library Synthesis

48B Billion REAL compounds and

Custom Library Synthesis

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On site access to all Enamine stock BB’s

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2 000 new building blocks are synthesized monthly. Here is an important update to our MedChem Highlights from February 2024

Recent News

  • 27 March 2024   Press Release

    Enamine Announces Expansion of Its Library Synthesis Capabilities

    March, 2024, Kyiv, Ukraine. Enamine Ltd, the global leader in supplying small molecules and early drug discovery services, announces the expansion of its library synthesis capabilities with a focus on Enamine REAL compounds to further support the growing demands of agricultural and pharmaceutical companies, research institutes, and drug discovery centers.

  • 01 March 2024   News

    Enamine and Genez International Announce Strategic Collaboration to Launch ...

    We are excited to announce a strategic collaboration between Enamine, the world's leading provider of chemical building blocks, compound libraries, and biology services, and Genez International, a prominent enterprise with 15 years of experience in cross-border supply management, biopharmaceutical research and development, semiconductor equipment, and high-definition digital imaging systems.

  • 21 February 2024   Press Release

    Cresset Announces Global Collaboration With Enamine on New Virtual ...

    Cresset recently announced a collaboration with Enamine, the world’s leading provider of chemical building blocks and drug discovery services to develop innovative new solutions for the early drug discovery process.

Upcoming events

J. Chem. Inf. Model. , 2013, 53 (8), 1990-2000

DOI: 10.1021/ci400213d

Tetko I. V.; Novotarskyi S.; Sushko I.; Ivanov V.; Petrenko A. E.; Dieden R.; Lebon F.; Mathieu B.

The dimethyl sulfoxide (DMSO) solubility data from Enamine and two UCB pharma compound collections were analyzed using 8 different machine learning methods and 12 descriptor sets. The analyzed data sets were highly imbalanced with 1.7-5.8% nonsoluble compounds. The libraries' enrichment by soluble molecules from the set of 10% of the most reliable predictions was used to compare prediction performances of the methods. The highest accuracies were calculated using a C4.5 decision classification tree, random forest, and associative neural networks. The performances of the methods developed were estimated on individual data sets and their combinations. The developed models provided on average a 2-fold decrease of the number of nonsoluble compounds amid all compounds predicted as soluble in DMSO. However, a 4-9-fold enrichment was observed if only 10% of the most reliable predictions were considered. The structural features influencing compounds to be soluble or nonsoluble in DMSO were also determined.

Development of Dimethyl Sulfoxide Solubility Models Using 163 000 Molecules: Using a Domain Applicability Metric to Select More Reliable Predictions

Tetko I. V.; Novotarskyi S.; Sushko I.; Ivanov V.; Petrenko A. E.; Dieden R.; Lebon F.; Mathieu B.
J. Chem. Inf. Model. 2013, 53 (8), 1990-2000
DOI: 10.1021/ci400213d

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