hERG toxicity prediction in early drug discovery using extreme gradient boosting and isometric stratified ensemble mapping

ABSTRACT

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 Numerous drugs have been withdrawn from the market due to cardiac side effects, underscoring the need for early identification of hERG toxicity.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.

KEYWORDS

hERG prediction; In Silico; Cardiotoxicity; ADMET; machine learning; XGBoost; supervised recursive variable selection.

AUTHORS

Gabriela Falcón-Cano†, Aliuska Morales-Helguera†, Heather Lambert†, Miguel Ángel Cabrera-Pérez ‡, and Christophe Molina†

†PIKAÏROS S.A, 31650 Saint Orens de Gameville, France

‡Departamento de Ciencias Farmacéuticas, Facultad de Ciencias, Universidad Católica del Norte, Angamos, 0610, Antofagasta, Chile

JOURNAL

Falcón-Cano, G., Morales-Helguera, A., Lambert, H. et al. hERG toxicity prediction in early drug discovery using extreme gradient boosting and isometric stratified ensemble mapping. Sci Rep 15, 15585 (2025). https://doi.org/10.1038/s41598-025-99766-3

SUPPORTING INFORMATION

hERG WEB SERVICE : https://www.hypercubane.re

Please get in touch with us through our Contact Page if you have any questions concerning the PIKAÏROS hERG web service.

THIS WEB SERVICE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.