We trained a quantitative structure-property relationship (QSAR) model to predict the lipophilicity of small molecules. The dataset consists of ∼63000 molecules with measured values in an in-house logD assay. Specifically, the downstream model is a multilayer perceptron (MLP) with two hidden layers and has been trained on the molecules' CDDD embeddings. The model performance is excellent with a cluster cross-validation coefficient of determination (R 2 ) score of 0.902. Upon testing on an independent dataset of 62 molecules, whose endpoints have been reviewed and curated from the Pesticide Properties Database , the final model led to an R 2 score of 0.914. All the XAI experiments and examples presented in this work are obtained from this final model, where the CDDD embeddings are generated through the Img2Mol encoder network. All the used input molecules are obtained from public data.