1 code implementation • 19 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.
1 code implementation • 19 Jun 2024 • Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian R. Jamasb, Charles Harris, Simon V. Mathis, Kieran Didi, Bryan Hooi, Pietro Liò
We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design.
1 code implementation • 3 Jun 2024 • Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio
In this work, we unify conditional training and sampling using the mathematically well-understood Doob's h-transform.
no code implementations • 14 Dec 2023 • Kieran Didi, Francisco Vargas, Simon V Mathis, Vincent Dutordoir, Emile Mathieu, Urszula J Komorowska, Pietro Lio
Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision.
2 code implementations • 14 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.
no code implementations • 23 Dec 2022 • Kieran Didi, Matej Zečević
Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction.
2 code implementations • 24 Oct 2022 • Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, Carla Gomes, Tom Blundell, Pietro Lio, Max Welling, Michael Bronstein, Bruno Correia
Here we show how a single pre-trained diffusion model can be applied to a broader range of problems, such as off-the-shelf property optimization, explicit negative design, and partial molecular design with inpainting.