In many cases, experimentally validated inactive (negative) samples are lacking in public databases, which leads to class imbalances in the available CPI data. This insufficiency leads to poor performance of ML models on out-of-domain samples and overestimation caused by majority class in CPI predictions. To compensate for the insufficiency of negative samples, the random pairing method (hereafter referred to as Random Negative)