The authors describe an innovative approach for designing novel inhibitors. This approach effectively integrates the emerging chemogenomics concept of target-family-based drug discovery with bioanalogous design strategies, including privileged structures, molecular frameworks as well as bioisosteric and bioanalogous/isofunctional modifications. The authors applied this method in the design of selective inhibitors of matrix metalloproteases (MMPs), also referred to as matrixins, on the basis of a unique analysis of the ligand-target knowledge base, the 'matrixinome'. For this analysis, the authors created an annotated MMP database containing ∼ 300 inhibitors with their published activity profile. The ligand space was then arranged into a lead evolution tree, where the substructural transformations in each virtual step led to marked changes in the activity pattern. This allowed subtype-specific privileged fragments to be extracted as well as modifications, which improve activity and/or selectivity. Furthermore, the compounds with the preferred activity profile were correlated with sequence homology as well as binding site similarity within the target family, thereby leading to the identification of substructural modifications that turn non-selective, biohomologous structures into selective inhibitors. The matrixinomic application of the authors' approach, therefore, provides an example of how the combination of ligand space knowledge with sequence-related data can radically improve the outcome of the lead optimisation process to achieve higher selectivity within a given target family.