REAL Compound Libraries
In addition to the full REAL database, we provide 5 million and 15 million diverse sets that represent the REAL drug-like space (compounds that comply with “rule of 5” and Verber criteria: MW≤500, SlogP≤5, HBA≤10, HBD≤5, rotatable bonds≤10, and TPSA≤140) and lack PAINS and toxic compounds.
Diverse REAL drug-like 5M set contains compounds that have no analogs with Tanimoto similarity more than 0.6 (Morgan 2 fingerprint, 512 bit) within the set and from Enamine stock screening compound collection. Diverse REAL drug-like 15M set less diverse, it contains compounds that have no analogs with Tanimoto similarity more than 0.65 (Morgan 2 fingerprint, 512 bit) within the set and from Enamine stock screening compound collection. Diverse REAL drug-like 5M set is included in diverse REAL drug-like 15M set. We prepared diverse REAL drug-like sets from the REAL drug-like set using MaxMin algorithm.
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 . Within the set, we have charted a “350/3” subset with compounds with most stringent physicochemical profiles to have high potency for optimization: 270≤MW≤350, 14≤heavy atoms≤26, SlogP≤3, and aryl rings≤2. PAINS and toxic compounds were removed.
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” 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: 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 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. 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 amino acids, 491K cpds, SDF
- REAL carboxylic acids, MW≤400, clogP≤3, 3.6M cpds, SMILES
- REAL lead-like aliphatic carboxylic acids 3.8M cpds, SMILES
- REAL lead-like aromatic carboxylic acids, 1.1M cpds, SMILES
- REAL lead-like aliphatic primary amines, 4.1M cpds, SMILES
- REAL lead-like aromatic primary amines, 3.5M cpds, SMILES
- REAL secondary amines, 8-21 heavy atoms, 8.4M cpds, SMILES
- REAL hydroxamates, 18K cpds, SDF
- REAL Terminal Acetylenes, 195K cpds, SMILES
REAL natural product-like compounds
We have utilizied an approach published by P. Ertl et. al to predict natural product-likeness of the REAL compounds. The REAL natural product-like compounds comprise drug-like molecules with the positive natural product-likeness score.