The rapid increase in experimental data along with recent progress in computational methods has brought modern biology a step closer toward solving one of the most challenging problems: prediction of protein function. Comprehension of protein function at its most basic level requires understanding of molecular interactions. Currently, it is becoming universally accepted that the scale of the accumulated data for analysis and for prediction necessitate highly efficient computational tools with appropriate application capabilities. The review presents the up-to-date advances in computational methods for structural pattern discovery and for prediction of molecular associations. We focus on their applications toward a range of biological problems and highlight the advantages of the combination of these methods and their integration with biological experiments. We provide examples, synergistically merging structural modeling, rigid and flexible structural alignment and detection of conserved structural patterns and docking (rigid and flexible with hinge-bending movements). We hope the review will lead to a broader utilization of computational methods, and their cross-fertilization with experiment.