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Table 1 Property distribution and origin of the 60,171 COCONUT entries with a DOI and annotated as plants, fungal, or bacterial

From: Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning

 

Plants NPsa

Fungal NPsa

Bacterial NPsa

MW ≤ 200b

7072 (21%)

4919 (31%)

2237 (21%)

200 ≤ MW < 800b

24,078 (71%)

10,111 (65%)

6066 (56%)

MW ≥ 800b

2622 (8%)

618 (4%)

2448 (23%)

Fsp3 ≤ 0.2c

4213 (13%)

1580 (10%)

1073 (10%)

0.2 ≤ Fsp3 < 0.8c

22,032 (65%)

11,334 (72%)

7986 (74%)

Fsp3 ≥ 0.8c

7527 (22%)

2734 (18%)

1692 (16%)

AlogP ≤ − 2d

4855 (14%)

373 (2%)

1446 (13%)

− 2 ≤ AlogP < 8d

28,315 (84%)

15,000 (96%)

8906 (83%)

AlogP ≥ 8d

602 (2%)

275 (2%)

399 (4%)

Glycosidese

8260 (24%)

797 (5%)

1793 (17%)

Peptidesf

194 (<1%)

676 (4%)

2053 (19%)

  1. aCOCONUT entries with a DOI and the specified taxonomical origin annotated; percentages refer to the total number of the selected entries within the specified class: 33,772 plants NPs, 15,648 fungal NPs, and 10,751 bacterial NPs
  2. bMolecular weight (MW) calculated with RDKit
  3. cFraction of sp3 (Fsp3) calculated with RDKit
  4. dOctanol: water partition coefficient calculated with RDKit following the Crippen method (AlogP)
  5. eContaining a cyclic N- or O-acetal substructure defined through SMARTS language
  6. fContaining a dipeptide substructure defined through SMARTS language
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Journal of Cheminformatics

ISSN: 1758-2946

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