Public articles linked to the same research event.
bioRxiv This study developed a graph-based QSAR modeling pipeline integrating assay data preprocessing, fingerprint and molecular graph feature representations, and benchmarking of classical machine learning, graph neural networks, graph transformers, and their consensus ensembles, applied to predict Caspase-3/7 activation, mitochondrial membrane potential disruption, and FDA drug-induced liver injury, where Graphormer achieved the highest F1 of 0.79 and the full consensus model achieved the highest AUC of 0.69 on DILI prediction, surpassing the previous best model with AUC 0.63 and F1 0.65, and identified structural motifs associated with dual activation and cell-line-specific responses through fragment enrichment analysis.
This study developed a graph-based QSAR modeling pipeline integrating assay data preprocessing, fingerprint and molecular graph feature representations, and benchmarking of classical machine learning, graph neural networks, graph transformers, and their consensus ensembles, applied to predict Caspase-3/7 activation, mitochondrial membrane potential disruption, and FDA drug-induced liver injury, where Graphormer achieved the highest F1 of 0.79 and the full consensus model achieved the highest AUC of 0.69 on DILI prediction, surpassing the previous best model with AUC 0.63 and F1 0.65, and identified structural motifs associated with dual activation and cell-line-specific responses through fragment enrichment analysis.
This study developed a graph-based QSAR modeling pipeline integrating assay data preprocessing, fingerprint and molecular graph feature representations, and benchmarking of classical machine learning, graph neural networks, graph transformers, and their consensus ensembles, applied to predict Caspase-3/7 activation, mitochondrial membrane potential disruption, and FDA drug-induced liver injury, where Graphormer achieved the highest F1 of 0.79 and the full consensus model achieved the highest AUC of 0.69 on DILI prediction, surpassing the previous best model with AUC 0.63 and F1 0.65, and identified structural motifs associated with dual activation and cell-line-specific responses through fragment enrichment analysis.
This study developed a graph-based QSAR modeling pipeline integrating assay data preprocessing, fingerprint and molecular graph feature representations, and benchmarking of classical machine learning, graph neural networks, graph transformers, and their consensus ensembles, applied to predict Caspase-3/7 activation, mitochondrial membrane potential disruption, and FDA drug-induced liver injury, where Graphormer achieved the highest F1 of 0.79 and the full consensus model achieved the highest AUC of 0.69 on DILI prediction, surpassing the previous best model with AUC 0.63 and F1 0.65, and identified structural motifs associated with dual activation and cell-line-specific responses through fragment enrichment analysis.