In silico study toward the identification of new and safe potential inhibitors of photosynthetic electron transport

Ribeiro, Taisa Pereira Piacentini; Manarin, Flávia; Melo, Eduardo Borges de

doi:10.1016/j.ecoenv.2018.02.016

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…[99][100][101][102]107 A QSA(P)R model is normally designed to predict medicinal (potency and pharmacokinetics of new pharmaceuticals, or their toxic effects), environmental (ecotoxicity and fate), and other (development of new agrochemicals, flavors, food ingredients, surfactants, etc.) [116][117][118] endpoints. It can also be used for regulatory purposes: a properly used model can predict whether a new chemical can be harmful to people and the environment even without the use of experimental techniques, so the registration of a chemical can be denied or sought, or new safety instructions can be deferred.…”

Section: Resultsmentioning

confidence: 99%

In silico Risk Assessment Studies of New Psychoactive Substances Derived from Amphetamines and Cathinones

Melo¹,

Martins²,

Rodrigues³

et al. 2020

J. Braz. Chem. Soc.

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The amount and variety of new psychoactive substances (NPS) are expanding, and there are difficulties in assessing their risks. In this regard, in silico methods are potentially useful to predict NPS properties faster and at a lower cost. In this work a quantitative structure-activity relationship (QSAR) model was used to verify the risk of drugs derived from amphetamines and cathinones. A dataset of 26 derivatives with in vitro affinity for norepinephrine transporter (NET) was selected. To ensure reproducibility of the results, only geometric molecular descriptors (AM1 (Austin model 1) level) obtained from the platform ChemDes and ordered predictors selection (OPS) were used. The model presents good internal statistics (n = 23; coefficient of determination (R 2) = 0.914). The small number of samples was divided into seven training sets (n = 17) and seven test sets (n = 6). The average R 2 pred = 0.754 showed that the model has good predictive capacity. Based on the tests, this model can accurately predict the risk range of three previously selected derivatives: methedrone (low), ethcathinone (medium), and methamphetamine (high), even when only data referring to NET are employed. We used these data to create a simple free program in Java that focuses on the risk assessment of recreational drugs belonging to this class of compounds.

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Section: Resultsmentioning

confidence: 99%

In silico Risk Assessment Studies of New Psychoactive Substances Derived from Amphetamines and Cathinones

Melo¹,

Martins²,

Rodrigues³

et al. 2020

J. Braz. Chem. Soc.

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“…[25][26][27] Up to date, there are dozens of reports about the successful application of molecular docking, pharmacophore modeling, QSAR modeling in drug and pesticide discovery. [28][29][30][31][32] However, studies reporting the discovery of novel compounds with herbicidal activity by rational in silico modeling are rather scarce.…”

Section: Introductionmentioning

confidence: 99%

Virtual screening of natural products against 5-enolpyruvylshikimate-3-phosphate synthase using the Anagreen herbicide-like natural compound library

et al. 2022

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In silico study toward the identification of new and safe potential inhibitors of photosynthetic electron transport

Cited by 2 publications

References 36 publications

In silico Risk Assessment Studies of New Psychoactive Substances Derived from Amphetamines and Cathinones

In silico Risk Assessment Studies of New Psychoactive Substances Derived from Amphetamines and Cathinones

Virtual screening of natural products against 5-enolpyruvylshikimate-3-phosphate synthase using the Anagreen herbicide-like natural compound library

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