LogPpred: An AI-Based Predictive Model for Accurate Estimation of Molecular LogP

Abstract

Lipophilicity, commonly described by the n-octanol/water partition coefficient (LogP), is a key physicochemical property influencing the pharmacokinetic behavior of small molecules. Reliable LogP estimation during the early stages of drug discovery is essential to support molecular design and prioritize compounds with favorable ADMET properties. In this work, we report the development of LogPpred, an AI-based predictor of molecular lipophilicity. Starting from a curated dataset of 13,536 molecules with experimentally determined LogP values, multiple machine learning algorithms and molecular representations were systematically evaluated. The best-performing model, based on Gaussian Process Regression and RDKit molecular descriptors, achieved a mean absolute error (MAE) of 0.34 on the independent internal test set. External validation on a fully independent OECD-derived dataset yielded an MAE of 0.84, demonstrating good generalization capability across diverse chemical space. Furthermore, experimental LogP determination of an additional set of independently selected compounds confirmed the predictive reliability of the model, yielding an MAE of 0.66. Comparative analyses showed that LogPpred outperformed several widely used LogP prediction tools. Applicability domain analysis further supported the reliability of the model, with MAE values improving to 0.28 and 0.62 for in-domain compounds in the internal and external validation sets, respectively. Overall, LogPpred represents a robust, accurate, and transparent tool for the early assessment of molecular lipophilicity in medicinal chemistry and drug discovery.