High-throughput prediction of cytochrome P450 (CYP) metabolism is an example where both transferability and applicability to open-shell structures are required. CYPs are a family of heme-containing enzymes that account for approximately 90% of the phase I drug metabolism. Consequently, computational tools capable of predicting sites of metabolism are instrumental in the drug development campaigns. Unlike most enzymes, where the active site is adapted for a specific substrate, the important CYP isoforms exhibit low specificity towards their substrates, with substrate reactivity playing a significant role in determining the sites of metabolism. This motivates a ligand-based approach, where the influence of the pro-tein active site environment is not taken into account mechanistically. Such an approach, as described by Tyzack and colleagues 46 is illustrated in Figure . In their work, the prediction of metabolic lability is based on the estimation of ligand reaction activation energy, along with CYP isoform-specific corrections based on site accessibility. The reaction activation energy is predicted from semi-empirical AM1 reaction energy and two subsequent corrections.