ArticleBiomolecules2020
Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning.
Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Assigning the stereochemistry of natural products by machine learning.Journal of cheminformatics · 2026Article
- An Assessment of Chemical Diversity in Microbial Natural Products.ACS central science · 2025Review
- Can large language models predict antimicrobial peptide activity and toxicity?RSC medicinal chemistry · 2024Article
- 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
- Structure Elucidation and Biosynthesis of Nannosterols A and B, Myxobacterial Sterols fromJournal of natural products · 2023Article
- Annotation of natural product compound families using molecular networking topology and structural similarity fingerprinting.Nature communications · 2023Article
- Discovering New Natural Products Using Metabolomics-Based Approaches.Advances in experimental medicine and biology · 2023Article
- Progress on open chemoinformatic tools for expanding and exploring the chemical space.Journal of computer-aided molecular design · 2022Article
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
- NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products.Journal of natural products · 2021Article
- Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning.Journal of cheminformatics · 2021Article
- Alkaloids in Contemporary Drug Discovery to Meet Global Disease Needs.Molecules (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.
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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.