Empowering natural product science with AI: leveraging multimodal data and knowledge graphs-Reference-Cited by-同舟云学术

Empowering natural product science with AI: leveraging multimodal data and knowledge graphs

Published:2024 Issue: Volume: Page:
ISSN:0265-0568
Container-title:Natural Product Reports
language:en
Short-container-title:Nat. Prod. Rep.

Author:

Meijer David¹^ORCID,Beniddir Mehdi A.²^ORCID,Coley Connor W.³^ORCID,Mejri Yassine M.²⁴,Öztürk Meltem⁴^ORCID,van der Hooft Justin J. J.¹^ORCID,Medema Marnix H.¹^ORCID,Skiredj Adam²^ORCID

Affiliation:

1. Bioinformatics Group, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, the Netherlands

2. Equipe “Chimie des Substances Naturelles”, Université Paris-Saclay, CNRS, BioCIS, 17 Avenue des Sciences, 91400 Orsay, France

3. Massachusetts Institute of Technology, Department of Chemical Engineering, USA

4. Université Paris Dauphine, PSL Research University, CNRS, Lamsade, 75016 Paris, France

Abstract

This viewpoint article promotes the ongoing efforts to organise natural product science within knowledge graphs, a promising approach for structuring training data for AI models capable of achieving human-level natural product anticipation.

Funder

H2020 European Research Council

Centre National de la Recherche Scientifique

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/NP/D4NP00008K

Reference23 articles.

1. Artificial intelligence for natural product drug discovery

2. Exploring chemical space using natural language processing methodologies for drug discovery

3. Computational approaches streamlining drug discovery

4. Computational planning of the synthesis of complex natural products

5. Natural product drug discovery in the artificial intelligence era