Post-genomic advances in bioinformatics have refined drug-design
strategies, by focusing on the reduction of serious side-effects
through the identification of enzymatic targets. We consider the
problem of identifying the enzymes (i.e., drug targets), whose
inhibition will stop the production of a given target set of
compounds, while eliminating minimal number of non-target
compounds. An exhaustive evaluation of all possible enzyme
combinations to find the optimal solution subset may become
computationally infeasible for very large metabolic networks. We
propose a scalable iterative algorithm which computes a sub-optimal
solution within reasonable time-bounds. Our algorithm is based on the
intuition that we can arrive at a solution close to the optimal one by
tracing backward from the target compounds. It evaluates immediate
precursors of the target compounds and iteratively moves backwards to
identify the enzymes whose inhibition will stop the production of the
target compounds while incurring minimum side-effects. We show that
our algorithm converges to a sub-optimal solution within a finite
number of such iterations. Our experiments on the E.Coli metabolic
network show that the average accuracy of our method deviates from
that of the exhaustive search only by 0.02 % . Our iterative
algorithm is highly scalable. It can solve the problem for the entire
metabolic network of E.Coli in less than 10 seconds.