Many of the drug candidates that fail in clinical trials are withdrawn because of unforeseen effects of human metabolism, such as toxicity and unfavorable pharmacokinetic profiles. Early pre-clinical elimination of such compounds is important but not yet possible. An ideal system would enable researchers to make a confident elimination decision based purely on the structure of a new compound, and incorporate and use multiple pre-clinical experimental data to support such a decision. Currently available resources can be split into three categories: (i). structure-activity relationships (SAR) computational models based on compound structure; (ii). 'pattern' databases of tissue or organ response to drugs, compiled from high-throughput experiments; and (iii). 'systems biology' databases of metabolic pathways, genes and regulatory networks. In this review, we outline the advantages and drawbacks of each of these systems and suggest directions for their integration.