Dataset: supplementary_table_s1_with_smiles_final.xlsx, 103.8 KB Access Condition: Open access Description: The synthetic organic herbicides with SMILES, simple molecular properties as well as assigned (legacy HRAC) and predicted modes of action and weed selectivity classes, plus applicability domain classes (English)
Dataset: supplementary_table_s2_with_smiles_final.xlsx, 41.27 KB Access Condition: Open access Description: The phytotoxic natural products with SMILES, simple molecular properties as well as predicted modes of action and weed selectivity classes (English)
Documentation: data_explanation.pdf, 109.36 KB Access Condition: Open access Description: Explanation of columns (English)
Please login to the repository to save this object to your list.
Cite this document
Stepanić, V., Oršolić, D. & Šmuc, T. (2023). Data from: Comprehensive machine learning based study of the chemical space of herbicides [Data set]. https://urn.nsk.hr/urn:nbn:hr:241:056193.
Stepanić, Višnja, et al. Data from: Comprehensive machine learning based study of the chemical space of herbicides. Institut Ruđer Bošković, 2023. 28 Oct 2024. https://urn.nsk.hr/urn:nbn:hr:241:056193.
Stepanić, Višnja, Davor Oršolić, and Tomislav Šmuc. 2023. Data from: Comprehensive machine learning based study of the chemical space of herbicides. Institut Ruđer Bošković. https://urn.nsk.hr/urn:nbn:hr:241:056193.
Stepanić, V., Oršolić, D. and Šmuc, T. 2023. Data from: Comprehensive machine learning based study of the chemical space of herbicides. Institut Ruđer Bošković. [Online]. [Accessed 28 October 2024]. Available from: https://urn.nsk.hr/urn:nbn:hr:241:056193.
Stepanić V, Oršolić D, Šmuc T. Data from: Comprehensive machine learning based study of the chemical space of herbicides. [Internet]. Institut Ruđer Bošković: Zagreb, Bijenička 54, HR; 2023, [cited 2024 October 28] Available from: https://urn.nsk.hr/urn:nbn:hr:241:056193.
V. Stepanić, D. Oršolić and T. Šmuc, Data from: Comprehensive machine learning based study of the chemical space of herbicides, Institut Ruđer Bošković, 2023. Accessed on: Oct 28, 2024. Available: https://urn.nsk.hr/urn:nbn:hr:241:056193.
Data from: Comprehensive machine learning based study of the chemical space of herbicides
Author
Višnja Stepanić Ruđer Bošković Institute
Author
Davor Oršolić Institut Ruđer Bošković Ruđer Bošković Institute
Author
Tomislav Šmuc Institut Ruđer Bošković Ruđer Bošković Institute
Scientific / art field, discipline and subdiscipline
NATURAL SCIENCES Biology Ecology
Abstract (english)
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 the refinement of herbicide
rotational program as well as for the 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.
Methods (english)
The data set HRAC2020 consists of 346 synthetic organic herbicides downloaded from the original HRAC list while its extended version of 509 herbicides contains additional 163 obsolete herbicides (HRAC_REST). The collected herbicides have relative molecular weight within the range 84–649. The additional 163 mostly obsolete herbicides were collected from the literature and open-source online databases: Compendium of Pesticide Common Names (http:// www. alanwood.net/ pesticides/), PPDB: Pesticide Properties Database, PubChem and PTID: Pesticide Target Interaction Database. The MoAs (Modes of Action) were assigned for 411 compounds according to the legacy HRAC system (314 herbicides from the HRAC2020 set). The data on application stage and weed selectivity (WS) were collected for subsets of 221 and 332 herbicides for HRAC2020 set and the whole set, respectively. The data set of 131 phytotoxic NPs was collected from literature as referred in the associated article.
The cleaned SMILES (the 5th column) were used as inputs for the calculations of 1D and 2D molecular descriptors shown in the 6th to 13th columns. They were calculated by the commercial software ADMET Predictor ver. 9.5 (Simulations Plus, Inc., USA) except RelPSA and Formal Charge which were calculated by the open-accessed program DataWarrior. The Formal Charge corresponds to the net ionization state of a molecule which was roughly estimated as a difference of numbers of basic nitrogen (pKa above 7.0) and acidic oxygen atoms (pKa below 7.0) calculated by DataWarrior.