23 citations · 38 across the 4 of their papers we have counts for
5 papers
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain +31
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a si…
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction
Dominic Masters, Josef Dean, Kerstin Klaser +7
This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task.…
Towards a Taxonomy of Graph Learning Datasets
Renming Liu, Semih Cantürk, Frederik Wenkel +10
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Although many different types of GNN mo…
Hierarchical graph neural nets can capture long-range interactions
Ladislav Rampášek, Guy Wolf
Graph neural networks (GNNs) based on message passing between neighboring nodes are known to be insufficient for capturing long-range interactions in graphs. In this project we stu…
Dr.VAE: Drug Response Variational Autoencoder
Ladislav Rampasek, Daniel Hidru, Petr Smirnov +2
We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction. Our models, Perturbation Variational Autoencoder and it…