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Validation of the model by external set of six compounds yielded a high (0.919) predictive value.
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Documents are generally represented for retrieval by either extracting index terms from them or by creating and selecting from an external set of candidate terms.
This external set of examples "supervises" the training process.
To validate the model by external test set, activities of 24 compounds were predicted and the residual values for external and internal data sets were evaluated (Table 2).
Both the models were validated by an external test set of eight compounds giving satisfactory predictive r2 values of 0.604 and 0.654, respectively.
Both models were validated by an external test set of eight compounds and gave satisfactory prediction (r2pred) of 0.772 and 0.701 for CoMFA and CoMSIA models, respectively.
For OBA analogues, the optimal model was validated by an external test set of six compounds with satisfactory predictive r2 value of 0.786.
This model was validated by an external test set of 6 compounds giving satisfactory predictive r2 value of 0.923 as well as the scrambling stability test.
Statistically significant models were established for 16 molecules and validated by an external test set of 5 compounds, yielding the best predictive DFT CoMFA model [r2 = 0.960, with 4 components, q2 = 0.707 and SEE = 1.12] and several CoMSIA models for AM1 and ab initio cases [r2 = 0.935 ÷ 0.947, q2 = 0.688 ÷ 0.739 and SEE = 1.32 ÷ 1.46].
The models were also validated by an external test set of four compounds with r of 0.821 (CoMFA) and r of 0.812 (CoMSIA).
In the next step, the CoMFA model was validated by the external test set of four compounds, ie, delphinidin, malvidin, pelargonidin-3,5-diglucoside, and peonidin-3-glucoside.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com