The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

In-silico prediction of aqueous solubility plays an important role during the drug discovery and development processes. For many years, the limited performance of in-silico solubility models has been attributed to the lack of high-quality solubility data for pharmaceutical molecules. However, some studies suggest that the poor accuracy of solubility prediction is not related to the quality of the experimental data and that more precise methodologies (algorithms and/or set of descriptors) are required for predicting aqueous solubility for pharmaceutical molecules. In this study a large and diverse database was generated with aqueous solubility values collected from two public sources; two new recursive machine-learning approaches were developed for data cleaning and variable selection, and a consensus model based on regression and classification algorithms was created. The modeling protocol, which includes the curation of chemical and experimental data, was implemented in KNIME, with the aim of obtaining an automated workflow for the prediction of new databases. Finally, we compared several methods or models available in the literature with our consensus model, showing results comparable or even outperforming previous published models.

Blockade of the human Ether-à-go-go Related Gene (hERG) potassium channel by small molecules can prolong the QT interval, leading to fatal cardiotoxicity. Numerous drugs have been withdrawn from the market due to cardiac side effects, underscoring the need for early identification of hERG toxicity. Despite several classification machine learning (ML) models having been developed to this end, robustness, class imbalance, and interpretability are still challenges. Using the largest public database of hERG inhibition, this work integrates eXtreme Gradient Boosting (XGBoost) with Isometric Stratified Ensemble (ISE) mapping (XGB + ISE map) to enhance hERG prediction. An XGBoost consensus model was developed using balanced training sets and diverse variable subsets, resulting in robust models less affected by class imbalance. The model demonstrated competitive predictive performance, achieving a balance between sensitivity (SE = 0.83) and specificity (SP = 0.90) through exhaustive validation. ISE mapping estimated the model applicability domain and improved prediction confidence evaluation and compound selection by stratifying data. Refined variable selection procedures enhanced model interpretability. Variable importance analysis highlights key molecular determinants (peoe_VSA8, ESOL, SdssC, MaxssO, nRNR2, MATS1i, nRNHR, nRNH2) associated with hERG inhibition. The XGB + ISE map strategy provides an effective approach to identifying promising molecules in drug discovery campaigns with reduced hERG inhibition risk.