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The goal of this Use Case is to evaluate a prediction algorithm: the algorithm trains a model on a training dataset, and then predicts the compounds of a test dataset towards a certain toxicology endpoint.
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Compound 26 was included in the set of compounds as a test of how a bulky group such as tert-butyloxycarbonyl (Boc) or potentially a fluorophore would be tolerated in binding to the receptor.
AChE data set of 95 compounds was divided into a training set of 30 compounds and a test set of 65 compounds, and BChE dataset of 77 compounds was divided into a training set of 26 compounds and a test set of 51 compounds, respectively, by taking into account its structural diversity and activity range.
The entire set of 156 compounds was divided into a training set of 127 compounds and a test set of 29 compounds according to Kennard and Stones algorithm.
To find the optimum values of two parameters (σ and ε) and prohibit the overfitting of the model, the dataset was separated into a training set of 40 compounds and a test set of 16 compounds randomly and the LOO cross-validation of the whole training set was performed.
The model was applied in predicting the activity of 74 compounds as a test set.
(ii) We took each compound subset as test compounds, and constructed a test set of compound protein pairs.
After intracerebroventricular administration of a test compound, the remaining amount of test compound in the CSF is determined.
By correlating classes of chemicals with biological activity, the program can tell researchers which features of a test compound are important.
Results from in vivo tests are, in many cases, decisive for the assessment of a mutagenic potential of a test compound.
In acute toxicity testing, a single dose of a test compound is given once to ascertain the adverse effects on the animal.
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