The best-performing method in the SAMPL9 blind challenge achieved an RMSE of 1.12 log P units . Thus, for predicting toluene/water partition coefficients, COSMO-RS has been shown to be more accurate than the GC and MD approaches . Machine Learning (ML) offers new possibilities for predicting toluene/water log P by utilizing experimental data. Recent advances in ML such as Graph Neural Networks (GNN) models and transformers enable end-to-end learning of molecular properties directly from the structure and have demonstrated success across various applications . The general idea is to find a representation of molecules, e.g., in the form of descriptors, strings, or graphs, which can be mapped to properties of interest by applying regressions methods. For instance, in predicting the toluene/water partition coefficient of APIs as a post-SAMPL9 study, used a variety of molecular descriptors -related to the topological structure and properties such as the Ghose-Crippen water/octanol partition coefficient -on which they fitted a multiple linear regression model for the 250 experimental log P values from their collected dataset. These 250 experimental toluene/water log P of neutral species are currently the largest available dataset in the literature. This multiple linear regression model achieved an RMSE of 1.05 log P units on the test dataset and an RMSE of 0.86 log P units on the SAMPL9 dataset . These promising results are constrained by the limited amount of training data, which may restrict the model's broader applicability and potentially its effectiveness across diverse solutes and log P ranges. The direct prediction of toluene/water log P of neutral species using ML therefore remains limited due to data scarcity, necessitating the exploration of alternative approaches.