In recent years, multiple experimental and clinical studies have substantiated the notion of "key" role of complement activation after major trauma in contributing to the deleterious pathophysiological sequelae in the injured brain, lungs, and musculoskeletal system. Complement activation furthermore significantly contributes to the mechanisms of systemic post-injury complications, such as I/R injury, sepsis, and multiple organ failure. Therapeutic options aimed at attenuating the inflammatory complications of major trauma are currently unsatisfactory, and research strategies have largely failed in extrapolation from "bench to bedside". Experimental data from recent animal studies highlight the potential for complement inhibitors aimed at targeting central complement components and specific complement activation products, as promising future pharmacological agents in patients with major trauma. In this regard, site-targeted complement inhibition by new generation chimeric molecules which link pharmacological inhibitors to the local site of complement activation and tissue deposition may represent the future pharmacological "golden bullet". These chimeric molecules act locally at the site of injury and inflammation, and thus avoid the unwanted negative and adverse effects of a systemic complement blockade. Clearly, there is a tremendous need for well-designed experimental studies to shed some further light into our understanding of the complement-mediated pathology of major trauma, with the hope of designing and implementing new clinical treatment strategies for severely injured patients in the near future. Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions. Metabolism refers to a collection of chemical reactions in vivo, which keep an unceasing supply of matter and energy for living organisms to maintain life (e.g., growth and reproduction) [1] . These energy-using and energy-releasing chemical reactions catalyzed by enzymes are organized into many metabolic pathways. Some compounds/small molecules play major roles in these pathways and are vital for many activities essential for life. For example, during the digestion, the energy rich molecules (i.e. carbohydrate) are broken apart to provide energy, which is then used by cells to build up complex molecules from simple molecules, such as utilizing amino acids to synthesize new proteins that the body needs. Identifying the biological functions of compounds is an effective way to study the mechanisms of many basic biological processes [2] . On the other hand, small molecules are the cause, and the cure, for many diseases. For example, diabetes mellitus is a metabolic disease caused by insufficient or inefficient insulin secretary response and elevated blood glucose level [3] . Compounds such as sulfonylureas [4] , acarbose [5] , biguanides, thiazolidinediones [5] , and sitagliptin [3] have been used as effective drugs for diabetic therapy. Therefore, it is essential to annotate the bioactivities of compounds, which will benefit drug design and disease treatment.