Natural products subsets: Generation and characterization
Natural products are attractive for drug discovery applications because of their distinctive chemical structures, such as an overall large fraction of sp3 carbon atoms, chiral centers (both features associated with structural complexity), large chemical scaffolds, and diversity of functional groups....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Artificial Intelligence in the Life Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667318523000107 |
| _version_ | 1797797094600212480 |
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| author | Ana L. Chávez-Hernández José L. Medina-Franco |
| author_facet | Ana L. Chávez-Hernández José L. Medina-Franco |
| author_sort | Ana L. Chávez-Hernández |
| collection | DOAJ |
| description | Natural products are attractive for drug discovery applications because of their distinctive chemical structures, such as an overall large fraction of sp3 carbon atoms, chiral centers (both features associated with structural complexity), large chemical scaffolds, and diversity of functional groups. Furthermore, natural products are used in de novo design and have inspired the development of pseudo-natural products using generative models. Public databases such as the Collection of Open NatUral ProdUcTs and the Universal Natural Product database (UNPD) are rich sources of structures to be used in generative models and other applications. In this work, we report the selection and characterization of the most diverse compounds of natural products from the UNPD using the MaxMin algorithm. The subsets generated with 14,994, 7,497, and 4,998 compounds are publicly available at https://github.com/DIFACQUIM/Natural-products-subsets-generation. We anticipate that the subsets will be particularly useful in building generative models based on natural products by research groups, particularly those with limited access to extensive supercomputer resources. |
| first_indexed | 2024-03-13T03:44:00Z |
| format | Article |
| id | doaj.art-d4c3b38eabb54b57bb4e2106619daf63 |
| institution | Directory Open Access Journal |
| issn | 2667-3185 |
| language | English |
| last_indexed | 2024-03-13T03:44:00Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Artificial Intelligence in the Life Sciences |
| spelling | doaj.art-d4c3b38eabb54b57bb4e2106619daf632023-06-23T04:45:03ZengElsevierArtificial Intelligence in the Life Sciences2667-31852023-12-013100066Natural products subsets: Generation and characterizationAna L. Chávez-Hernández0José L. Medina-Franco1DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, México City 04510, MexicoCorrespondence author.; DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, México City 04510, MexicoNatural products are attractive for drug discovery applications because of their distinctive chemical structures, such as an overall large fraction of sp3 carbon atoms, chiral centers (both features associated with structural complexity), large chemical scaffolds, and diversity of functional groups. Furthermore, natural products are used in de novo design and have inspired the development of pseudo-natural products using generative models. Public databases such as the Collection of Open NatUral ProdUcTs and the Universal Natural Product database (UNPD) are rich sources of structures to be used in generative models and other applications. In this work, we report the selection and characterization of the most diverse compounds of natural products from the UNPD using the MaxMin algorithm. The subsets generated with 14,994, 7,497, and 4,998 compounds are publicly available at https://github.com/DIFACQUIM/Natural-products-subsets-generation. We anticipate that the subsets will be particularly useful in building generative models based on natural products by research groups, particularly those with limited access to extensive supercomputer resources.http://www.sciencedirect.com/science/article/pii/S2667318523000107Artificial intelligenceChemical spaceChemical multiverseChiralityDe novo designDeep learning |
| spellingShingle | Ana L. Chávez-Hernández José L. Medina-Franco Natural products subsets: Generation and characterization Artificial Intelligence in the Life Sciences Artificial intelligence Chemical space Chemical multiverse Chirality De novo design Deep learning |
| title | Natural products subsets: Generation and characterization |
| title_full | Natural products subsets: Generation and characterization |
| title_fullStr | Natural products subsets: Generation and characterization |
| title_full_unstemmed | Natural products subsets: Generation and characterization |
| title_short | Natural products subsets: Generation and characterization |
| title_sort | natural products subsets generation and characterization |
| topic | Artificial intelligence Chemical space Chemical multiverse Chirality De novo design Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667318523000107 |
| work_keys_str_mv | AT analchavezhernandez naturalproductssubsetsgenerationandcharacterization AT joselmedinafranco naturalproductssubsetsgenerationandcharacterization |