229 citations · 371 across the 11 of their papers we have counts for
12 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…
Graph Positional and Structural Encoder
Semih Cantürk, Renming Liu, Olivier Lapointe-Gagné +4
Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, rendering them essential tools for empowering modern GNNs, and in particular graph…
Attending to Graph Transformers
Luis Müller, Mikhail Galkin, Christopher Morris +1
Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks…
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction
Dominic Masters, Josef Dean, Kerstin Klaser +9
We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction. Our model integrates a well-tuned local message passing compo…
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.…
Long Range Graph Benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin +4
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.…
Taxonomy of Benchmarks in Graph Representation Learning
Renming Liu, Semih Cantürk, Frederik Wenkel +9
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on de…
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi +3
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph…
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…
Dropout Feature Ranking for Deep Learning Models
Chun-Hao Chang, Ladislav Rampasek, Anna Goldenberg
Deep neural networks (DNNs) achieve state-of-the-art results in a variety of domains. Unfortunately, DNNs are notorious for their non-interpretability, and thus limit their applica…
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…