Biomolecule Design

gRNAde: Geometric Deep Learning for 3D RNA Inverse Design

gRNAde is a geometric deep learning pipeline for 3D RNA inverse design, analogous to ProteinMPNN. gRNAde outperforms physically-based Rosetta and is significantly faster. (Invited book chapter for Methods in Molecular Biology)

Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas, Charles Harris, Simon Mathis, Pietro Liò

gRNAde: Geometric Deep Learning for 3D RNA Inverse Design

Evaluating Representation Learning on the Protein Structure Universe

Open-source benchmarking framework for evaluating and scaling up protein structure representation learning methods, comparing protein language models and Geometric GNNs trained on AlphaFoldDB, the largest non-redundant protein structure corpus.

Arian R. Jamasb, Alex Moehead, Chaitanya K. Joshi, Zuobai Zhang, Kieran Didi, Simon V. Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Liò, Tom L. Blundell

Evaluating Representation Learning on the Protein Structure Universe

Multi-State RNA Design with Geometric Multi-Graph Neural Networks

We propose gRNAde, a geometric deep learning pipeline for 3D RNA inverse design from one or more backbone structures. (Invited book chapter for Methods in Molecular Biology)

Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas, Charles Harris, Simon Mathis, Pietro Liò

Multi-State RNA Design with Geometric Multi-Graph Neural Networks