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Fig. 5 a Membership relation between models and descriptors.
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Fig. 2 a Relationships among models and descriptors.
The relationship between the efficiency of the GP models and catchment descriptors (CDs) was investigated.
Finally, throughout R&D, exploring the relations between model components and virtual population descriptors on one hand and AB and NPE values on the other can help identify lacking or uncertain knowledge.
The interpretations reveal interesting trends within the data, support further mining of the dataset seeded by highlighted features and allow the user to understand the differences between models built from different descriptors.
Most of the published cases deal with QSAR models resulting from academic studies, for instance those studying a certain relationship between toxicity and a descriptor, proposing new descriptors as tools to better capture chemical structures, or describing a new algorithm to explore the possible links between toxicity and descriptors.
Fivefold CV was used during model optimization and descriptor selection.
A comparison between models trained on only compound descriptors ('Family QSAR') [62] and PCM permits to assess whether the use of GP improved the bioactivity modeling, by simultaneously modeling the target and the chemical spaces within a PCM study [6].
Specifically, QSPR models with four descriptors (4d QSPR models) and QSPR models with seven descriptors (7d QSPR models).
Finally, we attempted to examine the relative significance of the HTS "descriptors" in the resulting models and their interplay with chemical descriptors.
In training the models, the same ML method and descriptors are employed without any hand tuning or variable selection.
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