Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …

Abstract The Structural Genomics Consortium is an international open science research
organization with a focus on accelerating early-stage drug discovery, namely hit discovery …

Deep generative diffusion models are a promising avenue for de novo 3D molecular design
in material science and drug discovery. However, their utility is still constrained by …

Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …

The prediction of thermodynamic properties of carbon-based molecules based on their
geometrical conformation using fluctuation and density functional theories has achieved …

Mechanisms of protein-DNA interactions are involved in a wide range of biological activities
and processes. Accurately identifying binding sites between proteins and DNA is crucial for …

Generative models for 3D drug design have gained prominence recently for their potential to
design ligands directly within protein pockets. Current approaches, however, often suffer …

Including invariance of global properties of a phys-ical system as an intrinsic feature in
graph neural networks (GNNs) enhances the model's robustness and generalizability and …

Pre-trained models have been successful in many protein engineering tasks. Most notably,
sequence-based models have achieved state-of-the-art performance on protein fitness …

The point cloud is a flexible representation for a wide variety of data types, and is a
particularly natural fit for the 3D conformations of molecules. Extant molecule embedding …