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The current version of our toolkit provides machine learning models that are widely used in cheminformatics and drug discovery: (1) random forests, (2) support vector machines, and (3) artificial neural networks (single and multilayer).
Due to the small sample size, we used three approaches to mitigate the risk of false discovery: (1) We used nonparametric tests which are less sensitive and less powerful than parametric tests.
Discovery 1 -- Physical Photo credit: © DepositPhotos.com/@forewer.
(So far, we know that it worked on approach, with a nice set of the Galilean moons orbiting--moons independently discovered by Simon Marius, who wrote down his discovery 1 day after Galileo in January 1610, though Marius may have actually seen the moons first: months earlier.
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Natural products have been an important source for drug/lead discovery [1, 2].
It is, therefore, helpful for understanding cell pathways and facilitating drug discovery [1].
Predicting metabolictransformation is one of major challenges in drug discovery [1].
High throughput screening (HTS) has long been a paradigm in early-stage drug discovery [1].
Natural products from higher fungi origin are irreplaceable sources of architecturally distinct scaffolds for drug discovery [1, 2].
These reactions are widely applied in pharmaceutical chemistry for producing different structures and combinatorial libraries for drug discovery [1].
Understanding these interactions is thus critical for pharmaceutical and functional food industries particularly in the process of drug discovery [1].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com