ArticleScientific reports2021
Comprehensive machine learning based study of the chemical space of herbicides.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- QSAR modelling of pesticide modes of toxic action across trophic levels via multi-label extreme gradient boosting.Pest management science · 2026Article
- Natural Products from Marine Microorganisms with Agricultural Applications.Marine drugs · 2025Review
- Seaweed-based PPO inhibitors as a new frontier in biological weed control for sorghum cultivation: from ocean to field.Protoplasma · 2025Article
- Simplicity within biological complexity.Bioinformatics advances · 2025Article
- Review
- Molecular Targets of Herbicides and Fungicides─Are There Useful Overlaps for Fungicide Discovery?Journal of agricultural and food chemistry · 2023Review
- Novel and holistic approaches are required to realize allelopathic potential for weed management.Ecology and evolution · 2023Review
- Modern Approaches for the Development of New Herbicides Based on Natural Compounds.Plants (Basel, Switzerland) · 2023Review
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- cropCSM: designing safe and potent herbicides with graph-based signatures.Briefings in bioinformatics · 2022Article
- Hierarchical cluster analysis of herbicide modes of action reveals distinct classes of multiple resistance in weeds.Pest management science · 2022Article
- Biological Membrane-Penetrating Peptides: Computational Prediction and Applications.Frontiers in cellular and infection microbiology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Widespread use of herbicides results in the global increase in weed resistance. The rotational use of herbicides according to their modes of action (MoAs) and discovery of novel phytotoxic molecules are the two strategies used against the weed resistance. Herein, Random Forest modeling was used to build predictive models and establish comprehensive characterization of structure-activity relationships underlying herbicide classifications according to their MoAs and weed selectivity. By combining the predictive models with herbicide-likeness rules defined by selected molecular features (numbers of H-bond acceptors and donors, logP, topological and relative polar surface area, and net charge), the virtual stepwise screening platform is proposed for characterization of small weight molecules for their phytotoxic properties. The screening cascade was applied on the data set of phytotoxic natural products. The obtained results may be valuable for refinement of herbicide rotational program as well as for discovery of novel herbicides primarily among natural products as a source for molecules of novel structures and novel modes of action and translocation profiles as compared with the synthetic compounds.
Identifiers
What OpenQuestion holds
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.