  In this paper, we present a technique for balancing predictive relevance
models related to supervised modelling ligand biochemical activities to
biological targets. We train uncalibrated models employing conventional
supervised machine learning technique, namely Support Vector Machines.
Unfortunately, SVMs have a serious drawback. They are sensitive to imbalanced
datasets, outliers and high multicollinearity among training samples, which
could be a cause of preferencing one group over another. Thus, an additional
calibration could be required for balancing a predictive relevance of models.
As a technique for this balancing, we propose the Platt's scaling. The achieved
results were demonstrated on single-target models trained on datasets exported
from the ExCAPE database. Unlike traditional used machine techniques, we focus
on decreasing uncertainty employing deterministic solvers.
