Abstract
The presence of Activity Cliffs (ACs) has been known to represent a challenge for QSAR modeling. With its high data dependency, Machine Learning QSAR models will be directly influenced by the activity landscape. We propose several extended similarity and extended SALI methods to study the implications of ACs distribution on the training and test sets on the model's errors. Ununiform ACs and chemical space distribution tend to lead to worse models than the proposed uniform methods. ML modeling on AC-rich sets needs to be analyzed case-by-case. Proposed methods can be used as a tool to study the datasets, but as far as generalization, random splitting was the better-performing data splitting alternative overall.
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