ArticlePloS one2023
Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Predicting Antileishmanial Activity of Plant-Derived Compounds Using Random Forest Modeling.microPublication biology · 2026Article
- Machine learning and cheminformatics-based Identification of lichen-derived compounds targeting mutant PBP4Molecular diversity · 2025Article
- Cepharanthine InhibitsMicroorganisms · 2025Article
- Unraveling Nature's Pharmacy: Transforming Medicinal Plants into Modern Therapeutic Agents.Pharmaceutics · 2025Review
- Review
- Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation.International journal of molecular sciences · 2024Review
- In vitro and in silico prediction of antibacterial interaction between essential oils via graph embedding approach.Scientific reports · 2023Article
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant extract is a mixture of diverse phytochemicals, and considered as an important resource for drug discovery. However, large-scale exploration of the bioactive extracts has been hindered by various obstacles until now. In this research, we have introduced and evaluated a new computational screening strategy that classifies bioactive compounds and plants in semantic space generated by word embedding algorithm. The classifier showed good performance in binary (presence/absence of bioactivity) classification for both compounds and plant genera. Furthermore, the strategy led to the discovery of antimicrobial activity of essential oils from Lindera triloba and Cinnamomum sieboldii against Staphylococcus aureus. The results of this study indicate that machine-learning classification in semantic space can be a highly efficient approach for exploring bioactive plant extracts.
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Registered trials
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