In this work we report on the development of the Signature Molecular Descriptor (or Signature) for use in the solution of inverse design problems as well as in highthroughput screening applications. The ultimate goal of using Signature is to identify novel and non-intuitive chemical structures with optimal predicted properties for a given application. We demonstrate this in three studies: green solvent design, glucocorticoid receptor ligand design and the design of inhibitors for Factor XIa. In many areas of engineering, compounds are designed and/or modified in incremental ways which rely upon heuristics or institutional knowledge. Often multiple experiments are performed and the optimal compound is identified in this brute-force fashion. Perhaps a traditional chemical scaffold is identified and movement of a substituent group around a ring constitutes the whole of the design process. Also notably, a chemical being evaluated in one area might demonstrate properties very attractive in another area and serendipity was the mechanism for solution. In contrast to such approaches, computer-aided molecular design (CAMD) looks to encompass both experimental and heuristic-based knowledge into a strategy that will design a molecule on a computer to meet a given target. Depending on the algorithm employed, the molecule which is designed mightmore » be quite novel (re: no CAS registration number) and/or non-intuitive relative to what is known about the problem at hand. While CAMD is a fairly recent strategy (dating to the early 1980s), it contains a variety of bottlenecks and limitations which have prevented the technique from garnering more attention in the academic, governmental and industrial institutions. A main reason for this is how the molecules are described in the computer. This step can control how models are developed for the properties of interest on a given problem as well as how to go from an output of the algorithm to an actual chemical structure. This report provides details on a technique to describe molecules on a computer, called Signature, as well as the computer-aided molecule design algorithm built around Signature. Two applications are provided of the CAMD algorithm with Signature. The first describes the design of green solvents based on data in the GlaxoSmithKline (GSK) Solvent Selection Guide. The second provides novel non-steroidal glucocorticoid receptor ligands with some optimally predicted properties. In addition to using the CAMD algorithm with Signature, it is demonstrated how to employ Signature in a high-throughput screening study. Here, after classifying both active and inactive inhibitors for the protein Factor XIa using Signature, the model developed is used to screen a large, publicly-available database called PubChem for the most active compounds.« less