QSAR models work by transforming chemical structures into numerical descriptorsâsuch as molecular weight, hydrophobicity, and electronic propertiesâthat can be mathematically correlated with biological activity. Machine learning algorithms, including linear regression, decision trees, and neural networks, are then employed to develop predictive models. These models can identify which structural features contribute to anti-cancer activity, helping to prioritize compounds for further testing.
