One-dimensional molecular representations and similarity calculations: methodology and validation.

Abstract

Drug discovery research is increasingly dedicated to biological screening on a massive scale, which seems to imply a basic rejection of many computer-assisted techniques originally designed to add rationality to the early stages of discovery. While ever-faster and more clever 3D methodologies continue to be developed and rejected as alternatives to indiscriminant screening, simpler tools based on 2D structure have carved a stable niche in the high-throughput paradigm of drug discovery. Their staying power is due in no small part to simplicity, ease of use, and demonstrated ability to explain structure-activity data. This observation led us to wonder whether an even simpler view of structure might offer an advantage over existing 2D and 3D methods. Accordingly, we introduce 1D representations of chemical structure, which are generated by collapsing a 3D molecular model or a 2D chemical graph onto a single coordinate of atomic positions. Atoms along this coordinate are differentiated according to elemental type, hybridization, and connectivity. By aligning 1D representations to match up identical atom types, a measure of overall structural similarity is afforded. In extensive structure-activity validation tests, 1D similarities consistently outperform both Daylight 2D fingerprints and Cerius(2) pharmacophore fingerprints, suggesting that this new, simple means of representing and comparing structures may offer a significant advantage over existing tried-and-true methods.