8 citations · 9 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…
SigmaDock: Untwisting Molecular Docking With Fragment-Based SE(3) Diffusion
Alvaro Prat, Leo Zhang, Charlotte M. Deane +2
Determining the binding pose of a ligand to a protein, known as molecular docking, is a fundamental task in drug discovery. Generative approaches promise faster, improved, and more…
Learning Inter-Atomic Potentials without Explicit Equivariance
Ahmed A. Elhag, Arun Raja, Alex Morehead +6
Accurate and scalable machine-learned inter-atomic potentials (MLIPs) are essential for molecular simulations ranging from drug discovery to new material design. Current state-of-t…
An evaluation of unconditional 3D molecular generation methods
Martin Buttenschoen, Yael Ziv, Garrett M. Morris +1
Unconditional molecular generation is a stepping stone for conditional molecular generation, which is important in \emph{de novo} drug design. Recent unconditional 3D molecular gen…
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
Leo Klarner, Tim G. J. Rudner, Garrett M. Morris +2
Generative models have the potential to accelerate key steps in the discovery of novel molecular therapeutics and materials. Diffusion models have recently emerged as a powerful ap…
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…