Significant progress has been made in structure-based drug design by pharmaceutical companies at different stages of drug discovery such as identifying new hits, enhancing molecule binding affinity in hit-to-lead, and reducing toxicities in lead optimization. Drug metabolism is a major consideration for modifying drug clearance and also a primary source for drug metabolite-induced toxicity. With major cytochrome P450 structures identified and characterized recently, structure-based drug metabolism prediction becomes increasingly attractive. In silico methods based on molecular and quantum mechanics such as docking, molecular dynamics and ab initio chemical reactivity calculations bring us closer to understand drug metabolism and predict drug-drug interactions. In this study, we review important progress in drug metabolism and common in silico techniques adopted to predict drug regioselectivity, stereoselectivity, reactive metabolites, induction, inhibition and mechanism-based inactivation, as well as their implementation in hit-to-lead drug discovery.