2 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
Design-CP: Context Parallelism for Design of Protein Nanoparticles
Lorenzo Tarricone, Helen E. Eisenach, Aiko Muraishi +1
Many all-atom generative protein models can in principle design large multimeric complexes by jointly modelling all chains, but their quadratic token- and atom-pair representations…
On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints
Sam Money-Kyrle, Markus Dablander, Thierry Hanser +3
Molecular Graph Neural Networks (GNNs) are increasingly common in drug discovery, particularly for Quantitative Structure-Activity Relationship (QSAR) studies; yet, their superiori…
Molecular Representations for Large Language Models
Nicholas T. Runcie, Fergus Imrie, Charlotte M. Deane
Large Language Models (LLMs) are increasingly being used to support scientific discovery. In chemistry, tasks such as reaction prediction and structure elucidation require reasonin…
SigmaDock: Untwisting Molecular Docking With Fragment-Based SE(3) Diffusion
Alvaro Prat, Leo Zhang, Charlotte M. Deane +2
Determining the binding pose of a ligand to a protein, known as molecular docking, is a fundamental task in drug discovery. Generative approaches promise faster, improved, and more…
Assessing the Chemical Intelligence of Large Language Models
Nicholas T. Runcie, Charlotte M. Deane, Fergus Imrie
Large Language Models are versatile, general-purpose tools with a wide range of applications. Recently, the advent of "reasoning models" has led to substantial improvements in thei…