The recent advances in “omics” technologies have generated various in silico approaches for toxicity assessment. In silico-based toxicity predictions can overcome certain major drawbacks of laboratory experiments, including the limitation of conducting experiments in a chemical-by-chemical basis that can be expensive. This chapter discusses some recent applications of in silico approaches utilizing xenobiotic metabolism that can be used to assess the impact of cigarette smoke (CS). We first outline recent studies using quantum mechanics/molecular modeling and quantitative structure–activity relationships that focus on smoking-relevant cytochrome P450 (CYP) enzymes. Subsequently, we describe several network-based approaches for toxicity assessment and relevant use cases leveraging a xenobiotic metabolism network model for a quantitative assessment of CS impact.