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The QSPR model available was tested by an external test set consisting of 8 compounds.
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.
Both of the models were validated by an external test set, which gave a satisfactory predictive r2 value of 0.800 and 0.732, respectively.
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The models' predictive abilities were proved more valuable than the classical 2D-QSAR, and were further investigated by means of an external test set of 13 compounds.
In a recent paper Benigni et al. [26] pointed out that the prediction reliability should be checked by means of an external test set with new chemicals not used in modeling.
Our approach has been successfully validated by application to an external test set of 22 alanine mutations in the interface of Ras/RalGDS and outperformed the widely used CC/PBSA, FoldX, and Robetta methods [1].
We further validate the results by applying HSPred to an external test case, which is not part of the original data set.
Results on an external test set are also provided.
More suggestions(16)
by an external board
by an external lithium
by an external event
by an external consultant
by an external steel
by an external machine
by an external procurement
by an external system
by an online test
by an external explosion
by an electrochemical test
by an external agency
by an external magnet
by an orthogonal test
by an external reward
by an external actor
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