In this work, we model the problem of mining quasi-bicliques from weighted viral-host protein-protein interaction network as a biclustering problem for identifying strong interaction modules. In this regard, a multiobjective genetic algorithm-based biclustering technique is proposed that simultaneously optimizes three objective functions to obtain dense biclusters having high mean interaction strengths. The performance of the proposed technique has been compared with that of other existing biclustering methods on an artificial data. Subsequently, the proposed biclustering method is applied on the records of biologically validated and predicted interactions between a set of HIV-1 proteins and a set of human proteins to identify strong interaction modules. For this, the entire interaction information is realized as a bipartite graph. We have further investigated the biological significance of the obtained biclusters. The human proteins involved in the strong interaction module have been found to share common biological properties and they are identified as the gateways of viral infection leading to various diseases. These human proteins can be potential drug targets for developing anti-HIV drugs.