Determining the Predictive Limit of QSAR Models

The research done to evaluate how the predictivity of models are effected by error in either the training or the test set is simple to describe conceptually. Benchmark datasets are downloaded from reputable sources. Then the datasets are split into training and test sets. Randomized error is added and then models created on both error laden and native training sets. Those models are used to predict both error laden and native test sets. Differences in standard statistics commonly used to assess predictivity are observed. This dataset is associated with the following publication: Kolmar, S., and C. Grulke. The Effect of Noise on the Predictive Limit of QSAR Models. Journal of Cheminformatics. Springer, New York, NY, USA, 13: 92, (2021).

[
  "020:00"
]

[
  "020:000"
]

[
  "https://doi.org/10.1186/s13321-021-00571-7"
]