REAL Database Subsets

Practical way to start exploring REAL Database

REAL Diversity Set

Virtual screening of the ultra-large databases can be performed iteratively, starting with a small subset. Such a diverse subset can provide essential data to teach AI-based algorithms or already result in promising hits. We have created three REAL Diversity Sets (0.1%, 1%, and 10% of the REAL Database) that allow users to explore the REAL Database depending on their computational resources. The REAL Diversity Sets have molecules that comply with the Ro5 and Veber criteria: MW≤500, SlogP≤5, HBA≤10, HBD≤5, RotBonds≤10, and TPSA≤140 and fully represent the REAL Database. Once hits are identified, their REAL analogs can be found at


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REAL lead-like compounds

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

REAL fragments

Enamine has a large fragment collection in stock. REAL Database expands this fragment space allowing you to find novel compounds to grow and optimize found hits. We have selected REAL fragments by applying the Ro3 criteria (MW<300, SlogP≤3, HBA≤3, HBD≤3, RotBonds≤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≤HAC≤16, RotBonds≤3, and chiral centers≤1. PAINS and toxic compounds have been removed.


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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 have been removed.

REAL natural product-like compounds

We have utilized the approach published by P. Ertl, et al. to predict the natural product-likeness of the REAL compounds. The REAL natural product-like compounds comprise drug-like molecules with positive natural product-likeness scores.

REAL compounds


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