Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

Molecular property prediction is one of the fastest-growing applications of deep learning with
critical real-world impacts. Although the 3D molecular graph structure is necessary for …

Abstract Machine learning potentials are an important tool for molecular simulation, but their
development is held back by a shortage of high quality datasets to train them on. We …

Abstract Machine learning has transformed many fields and has recently found applications
in chemistry and materials science. The small datasets commonly found in chemistry …

De novo drug design aims to generate molecules from scratch that possess specific
chemical and pharmacological properties. We present a computational approach utilizing …

The application of computational approaches in drug discovery has been consolidated in
the last decades. These families of techniques are usually grouped under the common …

Machine learning has revolutionized many fields and has recently found applications in
chemistry and materials science. The small datasets commonly found in chemistry lead to …

Late-stage functionalization is an economical approach to optimize the properties of drug
candidates. However, the chemical complexity of drug molecules often makes late-stage …

Many molecular design tasks benefit from fast and accurate calculations of quantum-
mechanical (QM) properties. However, the computational cost of QM methods applied to …