Read‐Across Structure‐Property Relationship‐Based Superior Prediction of Fraction Unbound in Plasma from Chemical Structure: Interpretable Models with Minimum Descriptors

Accurately predicting the fraction unbound in plasma (f_up) from chemical structures is essential for understanding pharmacokinetic characteristics during the early stage of drug discovery. This prediction serves as a valuable tool for minimizing late-stage setbacks and refining subsequent screening processes. Conventional approaches often rely on complex computational methodologies that may require extensive descriptor sets, resulting in opaque models with limited interpretability. In this study, we applied the read-across strategy in combination with traditional quantitative structure-property relationship to predict f_up while minimizing descriptor complexity. Our method employs interpretable models (regression and classification), facilitating insight into the underlying structure-property relationships governing plasma protein binding. Through comprehensive validation and comparison with different machine learning methods, we demonstrated the superior predictive performance of quantitative read-across structure-property relationship multiple linear regression and classification-based read-across structure-property relatonship respectively. support vector classifier models across diverse chemical compounds. This approach offers a valuable tool for predicting f_up in the process of drug discovery. Overall, this study aims to advance the field of pharmacokinetic modeling by applying the read-across strategy that improves predictive power with interpretability. By elucidating the complex relationship between chemical structures and f_up, our best models have the potential to formulate more rational drug design approaches, ultimately contributing to the development of more effective therapeutics.