34 citations · 119 across the 17 of their papers we have counts for
6 papers · 1 filter
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi +7
Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms embedded as nodes in 3D Euc…
DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping
Jos Torge, Charles Harris, Simon V. Mathis +1
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. This approach pres…
Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
Charles Harris, Kieran Didi, Arian R. Jamasb +4
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.…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Group Invariant Global Pooling
Kamil Bujel, Yonatan Gideoni, Chaitanya K. Joshi +1
Much work has been devoted to devising architectures that build group-equivariant representations, while invariance is often induced using simple global pooling mechanisms. Little…
gRNAde: Geometric Deep Learning for 3D RNA inverse design
Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas +5
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 confo…