First broadly generalization diffusion model for 3D molecular generation. State-of-the-art results for periodic crystals and non-periodic molecular systems through transfer learning.

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)

A ‘Hitchhiker’s Guide’ for getting started with deep neural networks for 3D structural biology & chemistry – we wrote it without the goal of publishing at particular venues – as if we could travel back in time and learn from it if we were starting our PhDs today! 🤗

We propose a Geometric Weisfeiler-Leman test to study the expressive power of Geometric Graph Neural Networks and demonstrate the advantages of equivariant GNNs over invariant GNNs. (Oral presentation at NeurIPS 2022 Workshop)

A currated reading + watch list for beginners to get started with RNA modelling and desing.

Exploring the connection between Transformer models such as GPT and BERT for Natural Language Processing, and Graph Neural Networks. (80,000+ readers on The Gradient, featured in Probabilistic ML textbook and classes at Stanford, Cambridge, Oxford.)