> ## Documentation Index > Fetch the complete documentation index at: https://docs.revilico.bio/llms.txt > Use this file to discover all available pages before exploring further. # Solubility Prediction across 15 Solvents and Temperature > Predicted logS compared with about 39,000 measured solubilities from BigSolDB across 15 solvents between 298 and 373 K. How close are predicted solubilities to measured ones, and where do they fail? This page compares our solubility engine's predictions of logS (the base-10 logarithm of molar solubility) with the measurements in BigSolDB, a public collection of experimental solubilities, for 1,448 compounds in 15 solvents across a temperature range from 298 to 373 K. > [!NOTE] > > All predictions are ours. The measured values are BigSolDB's. Each measurement is compared with the engine's prediction for the same compound and solvent, interpolated to the temperature of that measurement. ## What the comparison shows Predictions track measurements well in the organic solvents: R² ranges from 0.47 to 0.68 across the 14 non-aqueous solvents, with mean absolute errors of about 0.4 to 0.8 logS. **Water is the exception.** There the engine's R² is 0.12 and its RMSE is 1.74 logS, and only 38% of predictions fall within one log unit, so aqueous solubility predictions from this engine should be treated with much more caution. Accuracy also falls above about 343 K, but only 575 of the 39,026 measurements are at those temperatures. ## By solvent Every solvent, with its measurement count under the name. Pick a class to focus on it. ## Predicted against measured Pick a solvent, or all of them. Each point is one measurement; the dashed line is perfect agreement. ## How the errors are distributed Prediction error across all measurements Prediction error across all measurements ## Does the engine know when it is wrong? Each prediction comes with an uncertainty. Splitting the measurements into four equal groups by predicted uncertainty shows whether it is informative. ## Temperature and molecular size ## How the comparison was made > **Predictions and measurements** > > The engine predicted logS for 1,448 compounds in 15 solvents (five polar protic, five polar aprotic and five non-polar) on a 10-point temperature grid from 298.15 to 373.15 K, 217,200 predictions in all. BigSolDB v2.0 holds about 104,000 measurements. We kept every measurement for a predicted compound and solvent between 298.15 and 373.15 K, which gives 39,026 measurements of 1,381 compounds. The other 67 compounds have no measurement in those solvents and that range. > **How the numbers are calculated** > > For each measurement, the engine's prediction is interpolated linearly in temperature between the two nearest grid points and compared with the measured logS. R² is the squared Pearson correlation over all points, and RMSE and MAE are in logS units. Every measurement counts once, so well-measured solvents such as ethanol weigh more in the overall figures than rarely measured ones. ## Reading these results > **A retrospective comparison** > > These are predictions compared with an existing database, not a blind prospective test. We have not been able to check whether any of the BigSolDB measurements were part of the data the engine was developed on, so read these results as agreement with published data, not as proof of accuracy on compounds the engine has never seen. > **No competing method is shown** > > This page reports the engine's accuracy on its own. It does not compare it with other solubility predictors, so it makes no claim about being better or worse than them. > **The result does not depend on how measurements are matched** > > The main figures interpolate each prediction to the temperature of the measurement. Restricting to measurements within 2 K of a prediction grid temperature, so that little or no interpolation is needed, gives R² of 0.53 and a mean absolute error of 0.60 logS on 23,875 measurements. Within 0.5 K it gives 0.52 and 0.62, and using only measurements at exactly 298.15 K it gives 0.53 and 0.62 on 4,968. Averaging repeated measurements of the same compound, solvent and temperature gives 0.53 and 0.59. In every case 78% to 79% of predictions are within one log unit. > **Measurements are heterogeneous** > > BigSolDB collects values from many laboratories and papers. Repeat measurements of the same compound can disagree by a good fraction of a log unit, which puts a floor under the error any predictor can reach. > **Water is harder** > > One possible reason is that aqueous solubility depends strongly on ionisation and on the solid form of the compound, neither of which is corrected for in the measurements used here. The data on this page cannot say whether that explains the gap. ## Sources * **Measured solubilities:** BigSolDB v2.0, a public database of experimental solubilities of compounds in organic solvents and water over a range of temperatures. * **Predictions:** Revilico solubility engine, 217,200 predictions, compared with BigSolDB as described above. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.