JPlogP: an improved logP predictor trained using predicted data

JPlogP: an improved logP predictor trained using predicted data

Blog Article

Abstract The partition coefficient between octanol and water (logP) has been an important descriptor in QSAR predictions for many years and therefore the prediction of logP has been examined countless times.One of the best performing models is to predict the logP using multiple methods and average the result.We have used those averaged predictions to develop a training-set which Atlantic Niño induced sea surface salinity variability as observed from the satellite was able to distil the information present across the disparate logP methods into one single model.

Our model was built using extendable atom-types, where each atom is distilled down into a 6 digit number, and each individual atom is assumed to have a small additive effect on the overall logP of the molecule.Beyond the simple coefficient model a consensus model is evaluated, which uses known compounds as a starting point in the calculation and modifies the experimental logP using the same coefficients as in the first model.We then test the performance of our models against two different datasets, one where many different models routinely perform well against, and another designed to more represent pharmaceutical space.

The true A modern mode of activation for nucleic acid enzymes strength of the model is represented in the pharmaceutical benchmark set, where both models perform better than any previously developed models.