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REAL Compound Libraries: New Chemical Space for Discovery

The chemical universe is immense. Have you ever thought that you can deal with virtual compounds as if they are from stock and benefit from low prices and assured quick delivery with minimum attrition? We can help you to escape from the availability bias and benefit from the huge diversity of drug-like compounds in your virtual screening campaigns and in search for hit analogues. Having over 135 000 building blocks in stock, we are ready at any moment to assemble the final molecules on your request in just 1 synthesis step. We set no limitations on the number of compounds to synthesize and allow cherry-picking in the entire database counting over 300 million synthetically feasible compounds. This database is called REAL to underline that the compounds are readily accessible.

We only need 2-3 weeks to ship you a library of ca. 100 compounds. Synthesis success rate normally attains 85% and higher. This is achieved because of the usage of only available in stock building blocks. Moreover, each such reagent has been previously subjected to test reactions profiling its reactivity. Together with thorough validated chemical reactions and elaborated SOP’s these factors make REAL database a unique product that allows for going beyond what is available in stock in the search for new hit compounds. Ordering REAL compounds is the same as if you order compounds from stock.

The current release of REAL database comprises over 300 million compounds that comply with “rule of 5”[1] and Verber[2] criteria: MW≤500, SlogP≤5, HBA≤10, HBD≤5, rotatable bonds≤10, and TPSA≤140.

In addition to the full REAL database, we provide 5 million and 30 million diverse sets that represent REAL drug-like space and lack PAINS and toxic compounds.

The database allows you to link the end structures with the corresponding building blocks. Besides mining the entire REAL database you can conveniently prepare its subset for further virtual screening by selecting first the preferred building blocks and then extract their derivatives in REAL database.

REAL lead-like compounds
The lead-like subset of REAL database has been obtained from the entire REAL database by filtration using the following molecular criteria: MW≤460, -4≤SlogP≤4.2, HBA≤9, HBD≤5, rings≤4, rotatable bonds≤10[3]. Within the set, we have charted a “350/3” subset with compounds with most stringent physicochemical profiles to have high potency for optimization:[4,5] 270≤MW≤350, 14≤heavy atoms≤26, SlogP≤3, and aryl rings≤2. PAINS and toxic compounds were removed.

REAL fragments
Enamine has a large fragment collection in stock. REAL database expands this fragment space allowing you to find novel fragments for your in-house collection and analogues of the found hits. We have prepared REAL Fragment collection by applying “rule of 3”[6] criteria (MW<300, SlogP≤3, HBA≤3, HBD≤3, rotatable bonds≤3, and TPSA≤60) to the entire REAL collection. We have also extracted a single pharmacophore subset that complies with even more stringent molecular selection criteria:[7] 140≤MW≤230, 0≤SlogP≤2, 10≤heavy atoms≤16, rotatable bonds≤3, and chiral centers≤1. PAINS and toxic compounds were removed.

REAL PPI modulators
Targeting protein-protein interactions (PPI) is a popular approach in modern drug discovery. Molecules in this REAL subset meet the reported criteria for PPI modulators[8] but they are not “greasy”. We included in the set large but not lipophilic molecules with 400≤MW≤700, SlogP≤4, HBA≤9, 3≤rings≤6, and Fsp3>0.35. Such focus is expected to provide large but yet soluble non-toxic compounds, ideal for iPPI research. PAINS and toxic compounds were removed.

REAL covalent modifiers
Molecules that can covalently bind to a target have been typically excluded from compound libraries and treated as toxic or PAINS. Appearance on the market of drugs with a covalent mechanism of action has initiated revision of this “orthodox” paradigm.[9] We have selected several sets of REAL covalent binders that involve popular warheads: sulfonyl fluoride, acrylamide, and boronic acid. For each warhead class, we provide a couple of sets. The first one contains molecules that comply with “rule of 5” and Veber criteria: MW≤500, SlogP≤5, HBA≤10, HBD≤5, rotatable bonds≤10, and TPSA≤140. The second set comprises fragments meeting “rule of 3” criteria: MW<300, SlogP≤3, HBA≤3, HBD≤3, rotatable bonds≤3, and TPSA≤60.

REAL compounds by chemical classes
Prefiltering REAL database by distinct structural motives that pop-up frequently in virtual screening significantly reduces computational time. We have created a number of REAL database subsets based on the presence of specific chemical moieties/pharmacophores in compound structures. PAINS and toxic compounds were removed.

REAL target-biased compounds
We have predicted the activity of REAL compounds based on the structural similarity (Tanimoto distance >0.5, Morgan 3 fingerprints, 2,048 bit) to the known bioactive compounds published in ChEMBL database.[10] The reference set was carefully curated to include only verified activities. The target-biased set contains potential ligands to 1 395 protein targets. PAINS and toxic compounds were removed.

REAL natural product-like compounds
We have utilizied an approach published by P. Ertl et. al[11] to predict natural product-likeness of REAL compounds. REAL natural product-like compounds comprise drug-like molecules with the ≥0.0 natural product-likeness score.

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  2. D. F. Veber, S. R. Johnson, H.-Y. Cheng, B. R. Smith, K. W. Ward, K. D. Kopple, J. Med. Chem. 2002, 45, 2615–2623.
  3. M. M. Hann, T. I. Oprea, Curr. Opin. Chem. Biol. 2004, 8, 255–263.
  4. A. Nadin, C. Hattotuwagama, I. Churcher, Angew. Chem. Int. Ed. Engl. 2012, 51, 1114–1122.
  5. T. J. Ritchie, S. J. F. Macdonald, Drug Discov. Today 2009, 14, 1011–1020.
  6. M. Congreve, R. Carr, C. Murray, H. Jhoti, Drug Discov. Today 2003, 8, 876–877.
  7. C. W. Murray, D. C. Rees, Angew. Chemie - Int. Ed. 2016, 55, 488–492.
  8. X. Morelli, R. Bourgeas, P. Roche, Curr. Opin. Chem. Biol. 2011, 15, 475–481.
  9. R. A. Bauer, Drug Discov. Today 2015, 20, 1061–1073.
  10. A. Gaulton, L. J. Bellis, A. P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani, et al., Nucleic Acids Res. 2012, 40, D1100–D1107.
  11. P. Ertl, S. Roggo, A. Schuffenhauer, J. Chem. Inf. Model. 2008, 48, 68–74.

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