A Comparative Study of QSPR Methods on a Unique Multitask PAMPA dataset

Andrs Formanek; Anna Vincze; Richrd Bicsak; Yves Moreau; Gyorgy T. Balogh; Adam Arany

cs.LGMay 1, 2026

A Comparative Study of QSPR Methods on a Unique Multitask PAMPA dataset

Authors: Andrs Formanek, Anna Vincze, Richrd Bicsak, Yves Moreau, Gyorgy T. Balogh, Adam Arany

Organizations: Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems,May Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium · Department of Artificial Intelligence and Systems Engineering, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary[cs.LG] · Department of Chemical and Environmental Process Engineering, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary · Center for Pharmacology and Drug Research & Development, Semmelweis University, Üllői Str. 26, H-1085 Budapest, Hungary · Department of Pharmaceutical Chemistry, Semmelweis University, Faculty of Pharmaceutical Sciences, Hőgyes Endre Street 7-9, H-1092 Budapest, Hungary

Abstract

We present a unique, multitask dataset comprising 143 drug and drug candidate molecules, each evaluated on in vitro, parallel artificial-membrane permeability assays (PAMPA) using six different model membranes. Using this resource, we systematically assess the effectiveness of various molecular descriptors and regression models in predicting passive membrane permeability. The studied models range from simple linear regression to a modern pre-trained transformer architecture. Particular attention is given to the trade-off between predictive performance and model interpretability, highlighting the challenges introduced by machine learning approaches. To our knowledge, this is the most comprehensive study on simultaneous modeling of multiple organ-specific PAMPA membranes to date, offering novel insights into membrane-specific permeability profiles. We found that expert-designed physico-chemical property descriptors are more fitting for a limited sample size permeabilty study than deep learning based representations.

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Sep 9, 2026 · cs.LGJ/K move · Enter open · S save

An Explainable Machine Learning Framework for Predicting Blood-Brain Barrier Permeability Using Molecular Descriptors

Fatemeh Mahmoudi

Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach their target sites within the brain. In this study, an explainable machine learning framework was developed to predict BBB permeability using molecular descriptors generated from the MoleculeNet BBBP dataset with the RDKit cheminformatics toolkit. Fifteen physicochemical descriptors extracted from 2,039 compounds were used to train four supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Hyperparameter optimization was performed using GridSearchCV, while model interpretability was investigated using SHapley Additive exPlanations (SHAP). Among the evaluated models, the optimized XGBoost classifier achieved the best predictive performance, with an accuracy of 88.97%, a precision of 88.92%, a recall of 97.76%, an F1-score of 93.13%, and a ROC-AUC of 0.9282. Stratified five-fold cross-validation further demonstrated the robustness of the proposed model, yielding a mean ROC-AUC of 0.8982 +/- 0.0130. Feature importance and SHAP analyses consistently identified TPSA, HBD, and LogP as the most influential molecular descriptors governing BBB permeability prediction. Overall, the proposed framework provides an accurate, interpretable, and computationally efficient approach for BBB permeability prediction and may serve as a valuable tool for the early-stage screening of CNS drug candidates.

Molecular Property Prediction Drug Discovery Molecular Structure Elucidation Model Interpretability Machine Learning

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Abstract

Explore similar work

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