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cs.LG2026
On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints
Sam Money-Kyrle, Markus Dablander, Thierry Hanser +3
Molecular Graph Neural Networks (GNNs) are increasingly common in drug discovery, particularly for Quantitative Structure-Activity Relationship (QSAR) studies; yet, their superiori…
cs.LG2024
Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report
Markus Dablander
Video games are a natural and synergistic application domain for artificial intelligence (AI) systems, offering both the potential to enhance player experience and immersion, as we…
cs.LG2024
Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction
Markus Dablander
Molecular featurisation refers to the transformation of molecular data into numerical feature vectors. It is one of the key research areas in molecular machine learning and computa…