A new simple and efficient molecular descriptor for the fast and accurate prediction of log P

The partition coefficient (log P) is a critical parameter that measures the balance between hydrophilicity and lipophilicity of molecules, playing a key role in molecular material design and drug development. Developing accurate, efficient, and computationally simple models for log P prediction is essential for advancing drug discovery and materials science. In this study, we introduce the optimized 3D molecular representation of structures based on electron diffraction descriptor (opt3DM) into machine learning (ML) frameworks, achieving highly accurate log P predictions. By fine-tuning key parameters, the scale factor (sL) and descriptor dimension (Ns), we identified the optimal values of sL = 0.5 and Ns = 500. Among various ML algorithms tested, automatic relevance determination (ARD) regression, Ridge regression, and Bayesian Ridge regression demonstrated superior predictive performance. These optimized models outperformed the OPEn structure-activity/property relationship app (OPERA) model on the M-dataset and also delivered competitive results in the SAMPL6 and SAMPL9 challenges. Our findings not only establish a robust, fast, and precise approach for log P prediction, but also highlight the potential of opt3DM as a powerful tool for molecular representation. This work lays a foundation for broader applications in molecular material design and drug development.