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Natural Product-likeness in ChEMBL

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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:

  1. How is Natural Product-likeness distributed in ChEMBL and how does this compare to Natural Product-likeness for "real" NPs?
  2. Can we observe any difference in Natural Product-likeness for compounds from different MedChem Journals in ChEMBL?
  3. How do typical Natural Products stored in ChEMBL look like?

In general, Natural Product-likeness for compounds stored in ChEMBL 32 ranges from -4.1 to 4.1 with a median value of around -1:

Comparison to "real" Natural Products

How does this relate to Natural Product-likeness of "real" Natural Products? To answer this question we need a ground truth dataset of Natural Products. We retrieved this dataset from ChEBI, our dictionary and ontology of Chemical Entities of Biological Interest, by querying for molecules that are defined as "metabolite" (CHEBI:25212) while at the same time not being a "drug metabolite" (CHEBI:49103) on the web interface ("Advanced Search" functionality):

ChEBI "Advanced Search" panel with the query used to retrieve the dataset of Natural Products

This gave a dataset of around 22K unique ChEBI molecules which are Natural Products. One third of these molecules (7,601) are included in ChEMBL 32. Their Natural Product-likeness distribution looks like this, with a median Natural Product-likeness score of 1.47:

So how does that compare to the distributions for ChEMBL compounds? Looks like the algorithm captures Natural Products well and that the distribution we found in ChEMBL resembles the distributions that the developers of the original algorithm for calculating Natural Product-likeness found:

ChEMBL NP-likeness distribution reflects well the original findings by Ertl et al.; graph on the right reprinted with permission from the American Chemical Society, J Chem Inf Model 2008 48 (1), 68-74, DOI: 10.1021/ci700286. Copyright 2008 American Chemical Society.

Next, let's look at typical chemical comppounds from this set of > 7K Natural Product compounds in ChEMBL. The structures that are displayed are those with most entries from this subset in ChEMBL 32. It appears that not all of them do also possess an appropriate Natural Product-likeness score. Uric Acid, for example, seems to ressemble features of syntehtic molecules, therefore displaying a score of only -0.57.

Typical Natural Products from ChEBI/ChEMBL and their NP-likeness score

Natural Product-likeness in different Journals

Another interesting aspect to study is the variation of focus of chemical research that is published in different Journals. We can anticipate that chemical structures published in the Journal of Natural Products are showing a higher Natural Product-likeness on average than compounds being published in classical MedChem journals without a specific focus on Natural Products.

Our analysis suggests that this assumption is indeed correct, with chemicals from J Nat Prod possessing a median Natural Product-likeness score of .1.91 while for the other ChEMBL core journals this value ranges between -0.66 and -1.61:




Here are some typical representatives of molecules extracted from J Nat Prod which do possess a high Natural Product-likeness score (>3):

Typical Natural Products from J Nat Prod and their NP-likness score

What's next?

We hope the new implementation of a Natural Product-likeness score is useful to the drug discovery community and that it will spike some interesting science questions. Of course there are alternative algorithms available and there are some databases that specialised on Natural Products. We aim to also include a new Natural Product flag in the future, in addition to the score for Natural Product-likeness. It will be interesting to see how well the two approaches are matching up. However, the success of using a predictive algorithm for calculating a score as well as the flagging of "real" Natural Products in ChEMBL will always depend upon the ground truth dataset for Natural Products.

 

 

 

 

 

 

 

 

 

 

 

 

 


 



 













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