Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers
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https://zenodo.org/record/6226319
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The 12 VS scenarios considered in this study employing six training-test data partitions (A-F). All training sets employ the same set of 371 actives (WO2015160641A2), but differ on the considered set of inactives and hence are uniquely identified by the latter (either TrueInactives, DeepCoys, RandomDecoys or ActivesOnly). Likewise, all test sets employ the same 297 actives (WO201503820A1), none of them also included in the training set, but different sets of inactives (TrueInactives or DeepCoys).
Table 1. Six virtual screening scenarios corresponding to six pairs of training-test data for each type of SFs (classification or regression)
Partition ID
Training set
Test set
Type
A
DeepCoys
TrueInactives
Classification
B
RandomDecoys
TrueInactives
Classification
C
ActivesOnly
TrueInactives
Classification
D
TrueInactives
DeepCoys
Classification
E
RandomDecoys
DeepCoys
Classification
F
ActivesOnly
DeepCoys
Classification
A
DeepCoys
TrueInactives
Regression
B
RandomDecoys
TrueInactives
Regression
C
ActivesOnly
TrueInactives
Regression
D
TrueInactives
DeepCoys
Regression
E
RandomDecoys
DeepCoys
Regression
F
ActivesOnly
DeepCoys
Regression
创建时间：
2023-06-12
