Search Results for author: Simon V. Mathis

Found 9 papers, 8 papers with code

Evaluating representation learning on the protein structure universe

1 code implementation19 Jun 2024 Arian R. Jamasb, Alex Morehead, Chaitanya K. Joshi, Zuobai Zhang, Kieran Didi, Simon V. Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Lio, Tom L. Blundell

We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks.

Representation Learning

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

1 code implementation12 Dec 2023 Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Liò, Yoshua Bengio, Michael Bronstein

In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations.

Protein Structure Prediction Specificity

DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping

1 code implementation14 Aug 2023 Jos Torge, Charles Harris, Simon V. Mathis, Pietro Lio

Scaffold hopping is a drug discovery strategy to generate new chemical entities by modifying the core structure, the \emph{scaffold}, of a known active compound.

Drug Discovery

Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?

2 code implementations14 Aug 2023 Charles Harris, Kieran Didi, Arian R. Jamasb, Chaitanya K. Joshi, Simon V. Mathis, Pietro Lio, Tom Blundell

Deep generative models for structure-based drug design (SBDD), where molecule generation is conditioned on a 3D protein pocket, have received considerable interest in recent years.

Benchmarking

gRNAde: Geometric Deep Learning for 3D RNA inverse design

2 code implementations24 May 2023 Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas, Charles Harris, Simon V. Mathis, Alex Morehead, Rishabh Anand, Pietro Liò

Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity.

3D geometry Diversity +1

On the Expressive Power of Geometric Graph Neural Networks

1 code implementation23 Jan 2023 Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò

The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test.

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