Recently, we included a Natural Product-likeness score for chemical compounds stored in ChEMBL. We made use of an algorithm published by Peter Ertl, Silvio Roggo and Ansgar Schuffenhauer in 2008 . Whereas the original version of this algorithm used a commercial data set of Natural Product molecules for training the algorithm (the CRC Dictionary of Natural Products) and an in-house library of synthetic molecules as a negative set, we used Greg Landrum's RDKit implementation which is based on ~50,000 natural products collected from various open databases and ~1 million drug-like molecules from ZINC as a "non-Natural Product background". After including the new score into ChEMBL, we were interested to see whether the results look meaningful. We had a handful of simple questions: How is Natural Product-likeness distributed in ChEMBL and how does this compare to Natural Product-likeness for "real" NPs? Can we observe any difference in Natural Product-likeness for
The ChEMBL-og
The Organization of Drug Discovery Data
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