66 citations · 79 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints
Markus Dablander, Thierry Hanser, Renaud Lambiotte +1
Extended-connectivity fingerprints (ECFPs) are a ubiquitous tool in current cheminformatics and molecular machine learning, and one of the most prevalent molecular feature extracti…
Exploring QSAR Models for Activity-Cliff Prediction
Markus Dablander, Thierry Hanser, Renaud Lambiotte +1
Pairs of similar compounds that only differ by a small structural modification but exhibit a large difference in their binding affinity for a given target are known as activity cli…
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning
Julius Berner, Markus Dablander, Philipp Grohs
We present a deep learning algorithm for the numerical solution of parametric families of high-dimensional linear Kolmogorov partial differential equations (PDEs). Our method is ba…