These include additive group methods, such as fragment-or atom-contribution approaches, and quantitative structure-property relationship (QSPR) methods. In atom contribution approaches, the log P is equal to the sum of contributions from the individual atom types multiplied by the number of occurrences of each in the molecule. These methods make the assumption that each atom contributes a certain amount to the solvation free energy and that these contributions are additive to the log P . In fragment (or group) contribution approaches, the log P is equivalent to the sum of the contributions from the fragment groups (more than a single atom), and typically uses correction terms that consider intramolecular interactions. These approaches are generally calculated by adding together the sum of the fragment contributions times the number of occurrences and the sum of the correction contributions times the number of occurrences in the molecule. The other class of empirical log P prediction approaches relies on QSPR. In QSPR, molecular descriptors are calculated and then used to make log P predictions. Descriptors can vary in complexity-some rely on simple counts of heteroatoms and carbon, while others are derived from correlating the 3D shape, electrostatic, and hydrogen bonding characteristics with the log P of the molecule. To find the log P, a regression model gets derived by fitting the descriptor contributions to experimental data. Machine learning approaches such as random forest models, deep neural network models, Gaussian processes, support vector machines, and ridge regression belong under this category.