Tabriz University of Medical Sciences Advanced Pharmaceutical Bulletin 2228-5881 13 4 2023 11 01 A Quantitative Structure-Activity Relationship for Human Plasma Protein Binding: Prediction, Validation and Applicability Domain 784 791 10.34172/apb.2023.078 EN Affaf Khaouane https://orcid.org/0000-0002-0145-8844 Samira Ferhat https://orcid.org/0000-0001-7923-4069 Salah Hanini https://orcid.org/0000-0002-9174-8545 Journal Article 10.34172/apb.2023.078 2022 05 24 2023 04 24 Purpose: The purpose of this study was to develop a robust and externally predictive in silico QSAR-neural network model for predicting plasma protein binding of drugs. This model aims to enhance drug discovery processes by reducing the need for chemical synthesis and extensive laboratory testing. Methods: A dataset of 277 drugs was used to develop the QSAR-neural network model. The model was constructed using a Filter method to select 55 molecular descriptors. The validation set’s external accuracy was assessed through the predictive squared correlation coefficient Q2 and the root mean squared error (RMSE). Results: The developed QSAR-neural network model demonstrated robustness and good applicability domain. The external accuracy of the validation set was high, with a predictive squared correlation coefficient Q2 of 0.966 and a root mean squared error (RMSE) of 0.063. Comparatively, this model outperformed previously published models in the literature. Conclusion: The study successfully developed an advanced QSAR-neural network model capable of predicting plasma protein binding in human plasma for a diverse set of 277 drugs. This model’s accuracy and robustness make it a valuable tool in drug discovery, potentially reducing the need for resource-intensive chemical synthesis and laboratory testing.