BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees

Moreira-Filho, José Teófilo; Braga, Rodolpho C.; Lemos, Jade Milhomem; Alves, Vinicius M.; Borba, Joyce Villa Verde Bastos; Costa, Wesley dos Santos; Kleinstreuer, Nicole; Muratov, Eugene; Andrade, Carolina Horta; Neves, Bruno Junior

doi:10.1016/j.ailsci.2021.100013

Cited by 21 publications

(19 citation statements)

References 69 publications

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“…In silico prediction of fig species (Ex-Fl and Ex-Fa) derivatives using BeeTox and rule-based filters for insecticide likeness predicted that 16 compounds were toxic to honeybees (A. mellifera), while 10 compounds did not follow the TICE rules for insecticide properties. BeeTox is an AI-based application that helps predict the acute toxicity of active derivatives (Moreira-Filho et al 2021). Similarly, rule-based filters for insecticide likeness help narrow down the selection to chemicals that exhibit features commonly associated with insecticidal activity and facilitate the detection of promising compounds (Radhakrishnan et al 2023).…”

Section: Discussionmentioning

confidence: 99%

“…BeeTox is an AI‐based application that helps predict the acute toxicity of active derivatives (Moreira‐Filho et al . 2021). Similarly, rule‐based filters for insecticide likeness help narrow down the selection to chemicals that exhibit features commonly associated with insecticidal activity and facilitate the detection of promising compounds (Radhakrishnan et al .…”

Section: Discussionmentioning

confidence: 99%

“…BeeTox is an artificial intelligence (AI)‐based free online server used to predict the acute toxicity of chemicals/ligands to honeybees (Moreira‐Filho et al . 2021). In the present study, BeeTox free online servers were used to predict the bee toxicities of selected compounds from Ex‐Fl and Ex‐Fa extracts.…”

Section: Methodsmentioning

confidence: 99%

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Insecticidal activities of two fig tree leaf extracts against the cotton pest Spodoptera litura (Fab.) and their impact on the non‐target red worm Eisenia foetida (Savigny)

Vasantha‐Srinivasan,

Shanmuga‐Priya,

Han

et al. 2023

Entomological Research

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Extracts of botanical origin naturally contain a complex mixture of chemicals considered effective in managing lepidopteran pests. Chemical screening of the ethanolic leaf extracts of two fig tree species, Ficus lyrata and Ficus auriculata, delivered 12 and 15 phyto‐compounds with relatively high peak area percentages in phytol and flavone, respectively. Larvicidal activity against Spodoptera litura yielded higher mortality rates at maximum‐concentration treatment (600 ppm) with Ficus lyrata (91.3%) and Ficus auriculata (98.5%) extracts during the second instar. Sub‐lethal dosages (300 ppm) of both Ficus lyrata and Ficus auriculata extracts impeded the development and reproduction of lepidopteran pests. The enzyme‐inhibition activity of both fig extracts elicited a significant reduction in the major digestive enzymes alkaline phosphatase, acid phosphatase, and lactate dehydrogenase dose‐responsively. Mid‐gut histological screening of Ficus lyrata and Ficus auriculata extracts displayed gut lumen disruptions, shape alterations of columnar cells, and brush‐border membrane damage. Further, the non‐target toxicity of fig extracts against the red worm Eisenia foetida was minimal compared with that of the chemical temephos. Overall, the Ficus extracts proved to be eco‐friendly strategies for managing the polyphagous pest Spodoptera litura and are potentially more sustainable and less harmful to non‐target earthworms. Nonetheless, in silico predictions suggest that the active compounds in fig extracts are predominantly toxic against honeybees (16 compounds) and violate TICE rules (10 compounds). Therefore, the biological actions of fig extracts' individual novel chemistries need to be examined on target and non‐target species to develop better pest management strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Insecticidal activities of two fig tree leaf extracts against the cotton pest Spodoptera litura (Fab.) and their impact on the non‐target red worm Eisenia foetida (Savigny)

Vasantha‐Srinivasan,

Shanmuga‐Priya,

Han

et al. 2023

Entomological Research

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“…A wide range of machine learning (ML) approaches allows for explaining the chemistry of molecules, attributing which parts of the molecules are responsible for the chemical property of interest [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] , and lessening the black box challenge of machine learning 20,21 . Typical explainable ML approaches that provide atomwise attribution include dummy atoms 22 , classification of atoms by chemical intuition 23 , regression models 24 , graph neural network (GNN) attributions [25][26][27][28] with gradients 29 , perturbations 30 , decompositions 31 , and surrogates 32 .…”

Section: Background and Summarymentioning

confidence: 99%

Ground truth explanation dataset for chemical property prediction on molecular graphs

Hruska

Zhao

Liu

2022

Preprint

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Interpretation of chemistry on an atomic scale improves with explainable artificial intelligence (XAI). The parts of the molecule with the most significant influence on the chemical property of interest can be visualized with atomwise and bondwise attributions. Nonetheless, the attributions from different XAI methods regularly disagree substantially, causing uncertainty about which explainability is correct. To determine a ground truth for attributions, we define chemical operations which avoid alchemical steps or approximations and allow extracting one attribution per atom or bond from existing datasets of chemical properties. This general procedure allows generating large datasets of ground truth attributions. The approach allowed us to create a ground truth explanation dataset with more than 5 million data points for the HOMO-LUMO gap chemical property. This open-source dataset of atomistic ground truth explainability may serve as a reference for XAI approaches.

show abstract

Section: Background and Summarymentioning

confidence: 99%

Ground truth explanation dataset for chemical property prediction on molecular graphs

Hruska

Zhao

Liu

2022

Preprint

View full text Add to dashboard Cite

Interpretation of chemistry on an atomic scale improves with explainable artificial intelligence (XAI). The parts of the molecule with the most significant influence on the chemical property of interest can be visualized with atomwise and bondwise attributions. Nonetheless, the attributions from different XAI methods regularly disagree substantially, causing uncertainty about which explanation is correct. To determine a ground truth for attributions, we define chemical operations which avoid alchemical steps or approximations and allow extracting one attribution per atom or bond from existing datasets of chemical properties. This general procedure allows for generating large datasets of ground truth attributions. The approach allowed us to create a ground truth explanation dataset with more than 5 million data points for the HOMO-LUMO gap chemical property. This open-source dataset of atomistic ground truth explanations may serve as a reference for XAI approaches.

show abstract

BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees

Cited by 21 publications

References 69 publications

Insecticidal activities of two fig tree leaf extracts against the cotton pest Spodoptera litura (Fab.) and their impact on the non‐target red worm Eisenia foetida (Savigny)

Insecticidal activities of two fig tree leaf extracts against the cotton pest Spodoptera litura (Fab.) and their impact on the non‐target red worm Eisenia foetida (Savigny)

Ground truth explanation dataset for chemical property prediction on molecular graphs

Ground truth explanation dataset for chemical property prediction on molecular graphs

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