This is our first attempt at classifying the shapes of drug molecules, and our goal is to provide a “high-level overview” of the gross structural features of these molecules. Accordingly, for purposes of this research, we have deliberately defined “shape” in simple terms. The first classification scheme ignores such important features as the details of substituents on rings, chain branching, bond order, atom types, stereochemistry, and three-dimensional conformation. The second classification method does account for bond order and atom types. There is no reason to believe that the set of 5120 molecules in our database represents all the possible shapes that a drug may take. However, it is instructive to examine the universe of known drugs to see what patterns may exist. Once these patterns have been deduced, the drug designer may apply them in various ways. For example, one might attempt to bias a de novo design program or a combinatorial chemistry effort to produce a set of molecules which either contains or does not contain these patterns. The reader must bear in mind that “shape” in this work refers to the two-dimensional topological graph of the molecules. While three-dimensional shape is partially encoded in the two-dimensional graph of a molecule, we expect that the three-dimensional conformations of drugs with the same topological shape will not all be similar, although certain conformations would be expected to appear more frequently than others. Of course, the preferences we have identified for certain shapes do not necessarily reveal some fundamental truth about drugs, receptors, metabolism, or toxicity. Instead, it may reflect the constraints imposed by the scientists who have produced these drugs. Constraints due to synthetic or patent considerations, cost, or a general conservatism (i.e., a tendency to make