Search Results for author: Kieran Didi

Found 7 papers, 5 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

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

On How AI Needs to Change to Advance the Science of Drug Discovery

no code implementations23 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.

Drug Discovery Protein Structure Prediction

Structure-based Drug Design with Equivariant Diffusion Models

2 code implementations24 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.

Specificity

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