This paper describes a simple method for estimating the aqueous solubility (ESOL--Estimated SOLubility) of a compound directly from its structure. The model was derived from a set of 2874 measured solubilities using linear regression against nine molecular properties. The most significant parameter was calculated logP(octanol), followed by molecular weight, proportion of heavy atoms in aromatic systems, and number of rotatable bonds. The model performed consistently well across three validation sets, predicting solubilities within a factor of 5-8 of their measured values, and was competitive with the well-established "General Solubility Equation" for medicinal/agrochemical sized molecules.

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This work provides a comprehensive overview of agrochemical properties in terms of the way they change during progression from screen hit to product and in terms of their limits as expressed in modern commercial products. Most herbicides and fungicides readily meet the Lipinski 'rule of five' criteria for drug-like compounds with many meeting the more constrained limits reported for pharmaceutical leads.

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A method for assessing the biological discriminating power of chemical similarity measures is presented. The main concern of this work was to develop an objective way of evaluating different similarity measures in terms of how well they distinguished between active and inactive compounds. In addition, we have explored the level of similarity required for optimal separation and commented on its implications for work in the field of chemical diversity studies. The results for one simple similarity measure showed that statistically significant separation could be achieved, and indicated a reasonable similarity value for future work.