ArticleBiomolecules2019
NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules.
Article in Biomolecules, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 69 citations in OpenAlex.
- Integrating data augmentation and BERT-based deep learning for predicting alpha-glucosidase inhibitors derived from Black Cohosh.Scientific reports · 2025Article
- Unraveling Nature's Pharmacy: Transforming Medicinal Plants into Modern Therapeutic Agents.Pharmaceutics · 2025Review
- Fragment Libraries from Large and Novel Synthetic Compounds and Natural Products: A Comparative Chemoinformatic Analysis.ACS omega · 2025Article
- Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.Journal of medicinal chemistry · 2025Review
- Predicting and Explaining Yields with Machine Learning for Carboxylated Azoles and Beyond.Journal of chemical information and modeling · 2025Article
- Time-Dependent Comparison of the Structural Variations of Natural Products and Synthetic Compounds.International journal of molecular sciences · 2024Article
- Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products.Natural product reports · 2024Review
- A Multi-Label Learning Framework for Predicting Chemical Classes and Biological Activities of Natural Products from Biosynthetic Gene Clusters.Journal of chemical ecology · 2023Article
- Artificial intelligence for natural product drug discovery.Nature reviews. Drug discovery · 2023Review
- School of cheminformatics in Latin America.Journal of cheminformatics · 2023Article
- Putative target sites in synganglion for novel ixodid tick control strategies.Ticks and tick-borne diseases · 2023Review
- A Reinforcement Learning Framework to Discover Natural Flavor Molecules.Foods (Basel, Switzerland) · 2023Article
- The Time and Place for Nature in Drug Discovery.JACS Au · 2022Review
- Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data.Scientific reports · 2022Article
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
- Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning.Journal of cheminformatics · 2021Article
- Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop.Journal of cheminformatics · 2021Article
- Alkaloids in Contemporary Drug Discovery to Meet Global Disease Needs.Molecules (Basel, Switzerland) · 2021Review
- SANCDB: an update on South African natural compounds and their readily available analogs.Journal of cheminformatics · 2021Article
- Natural product fragment combination to performance-diverse pseudo-natural products.Nature communications · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural products (NPs) remain the most prolific resource for the development of smallmolecule drugs. Here we report a new machine learning approach that allows the identification of natural products with high accuracy. The method also generates similarity maps, which highlight atoms that contribute significantly to the classification of small molecules as a natural product or synthetic molecule. The method can hence be utilized to (i) identify natural products in large molecular libraries, (ii) quantify the natural product-likeness of small molecules, and (iii) visualize atoms in small molecules that are characteristic of natural products or synthetic molecules. The models are based on random forest classifiers trained on data sets consisting of more than 265,000 to 322,000 natural products and synthetic molecules. Two-dimensional molecular descriptors, MACCS keys and Morgan2 fingerprints were explored. On an independent test set the models reached areas under the receiver operating characteristic curve (AUC) of 0.997 and Matthews correlation coefficients (MCCs) of 0.954 and higher. The method was further tested on data from the Dictionary of Natural Products, ChEMBL and other resources. The best-performing models are accessible as a free web service at http://npscout.zbh.uni-hamburg.de/npscout.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.