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Ladislav Rampasek

Alumni

Publications

Graph Positional and Structural Encoder
Renming Liu
Semih Cantürk
Olivier Lapointe-Gagné
Vincent Létourneau
Guy Wolf
Dominique Beaini
Ladislav Rampasek
Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node … (voir plus)ordering. This renders PSEs essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for a variety of graph prediction tasks is a challenging and unsolved problem. Here, we present the graph positional and structural encoder (GPSE), a first-ever attempt to train a graph encoder that captures rich PSE representations for augmenting any GNN. GPSE can effectively learn a common latent representation for multiple PSEs, and is highly transferable. The encoder trained on a particular graph dataset can be used effectively on datasets drawn from significantly different distributions and even modalities. We show that across a wide range of benchmarks, GPSE-enhanced models can significantly improve the performance in certain tasks, while performing on par with those that employ explicitly computed PSEs in other cases. Our results pave the way for the development of large pre-trained models for extracting graph positional and structural information and highlight their potential as a viable alternative to explicitly computed PSEs as well as to existing self-supervised pre-training approaches.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov
Cheng-Hao Liu
Moksh J. Jain
Almer M. van der Sloot
Eric Jolicoeur
Edward Ruediger
Andrei Cristian Nica
Emmanuel Bengio
Kostiantyn Lapchevskyi
Daniel St-Cyr
Doris Alexandra Schuetz
Victor I Butoi
Jarrid Rector-Brooks
Simon R. Blackburn
Leo Feng
Hadi Nekoei
Sai Krishna Gottipati
Priyesh Vijayan
Prateek Gupta
Ladislav Rampasek … (voir 14 de plus)
Sasikanth Avancha
Pierre-Luc Bacon
William L. Hamilton
Brooks Paige
Sanchit Misra
Stanisław Jastrzębski
Bharat Kaul
Doina Precup
José Miguel Hernández-Lobato
Marwin Segler
Michael M. Bronstein
Anne Marinier
Mike Tyers
Yoshua Bengio
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (voir plus)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.
2024-05-02
ArXiv (prépublication)
doi.org
arxiv.org
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction
Dominic Masters
Josef Dean
Kerstin Klaser
Zhiyi Li
Samuel Maddrell-Mander
Adam Sanders
Hatem Helal
Deniz Beker
Andrew William Fitzgibbon
Shenyang Huang
Ladislav Rampasek
Dominique Beaini
2023-07-30
TMLR (accepté)
doi.org
openreview.net
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction
Dominic Masters
Josef Dean
Kerstin Klaser
Zhiyi Li
Samuel Maddrell-Mander
Adam Sanders
Hatem Helal
Deniz Beker
Ladislav Rampasek
Dominique Beaini
2022-11-18
ArXiv (prépublication)
doi.org
arxiv.org
Long Range Graph Benchmark
Vijay Prakash Dwivedi
Ladislav Rampasek
Mikhail Galkin
Ali Parviz
Guy Wolf
Anh Tuan Luu
Dominique Beaini
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to b… (voir plus)uild node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or necessary for learning a given task on graphs. Recently, there has been an increasing interest in development of Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI. However, MP-GNNs that simply rely on 1-hop message passing often fare better in several existing graph benchmarks when combined with positional feature representations, among other innovations, hence limiting the perceived utility and ranking of Transformer-like architectures. Here, we present the Long Range Graph Benchmark (LRGB) with 5 graph learning datasets: PascalVOC-SP, COCO-SP, PCQM-Contact, Peptides-func and Peptides-struct that arguably require LRI reasoning to achieve strong performance in a given task. We benchmark both baseline GNNs and Graph Transformer networks to verify that the models which capture long-range dependencies perform significantly better on these tasks. Therefore, these datasets are suitable for benchmarking and exploration of MP-GNNs and Graph Transformer architectures that are intended to capture LRI.
openreview.net
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampasek
Mikhail Galkin
Vijay Prakash Dwivedi
Anh Tuan Luu
Guy Wolf
Dominique Beaini
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art result… (voir plus)s on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack a common foundation about what constitutes a good positional or structural encoding, and what differentiates them. In this paper, we summarize the different types of encodings with a clearer definition and categorize them as being
openreview.net