A two-step computational method for designing new molecules in medicinal chemistry is described. In the first step, topological indices are used to develop structure-based correlations for properties of interest. Zeroth and first order connectivity indices are employed to develop linear correlations for three physical properties of interest in pharmaceutical chemistry: octanol-water partition coefficient (OWPC), melting point and water solubility. These correlations are then used within an optimization framework to design molecules having the desired properties. This step involves formulating a mixed integer linear program (MILP) which includes the property correlations, structural constraints which ensure that a stable, connected molecule is formed, and an objective function which minimizes the deviation from a set of property targets. A new data structure, known as a partitioned adjacency matrix, is employed to allow the connectivity index definitions to be written linearly, such that they can be included in an MILP and solved using a standard branch-and-bound method. The connectivity of the molecule is ensured by the inclusion of network flow constraints within the formulation. Three examples show the efficacy of this approach. (C) 2003 Elsevier Ltd. All rights reserved.