Structure-based drug design with geometric deep learning
Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: a review
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, there has been a …
affinities has the potential to transform drug discovery. In recent years, there has been a …
Spice, a dataset of drug-like molecules and peptides for training machine learning potentials
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 …
development is held back by a shortage of high quality datasets to train them on. We …
OpenMM 8: molecular dynamics simulation with machine learning potentials
Machine learning plays an important and growing role in molecular simulation. The newest
version of the OpenMM molecular dynamics toolkit introduces new features to support the …
version of the OpenMM molecular dynamics toolkit introduces new features to support the …
Learning matter: Materials design with machine learning and atomistic simulations
Conspectus Designing new materials is vital for addressing pressing societal challenges in
health, energy, and sustainability. The combination of physicochemical laws and empirical …
health, energy, and sustainability. The combination of physicochemical laws and empirical …
GemNet-OC: develo** graph neural networks for large and diverse molecular simulation datasets
Recent years have seen the advent of molecular simulation datasets that are orders of
magnitude larger and more diverse. These new datasets differ substantially in four aspects …
magnitude larger and more diverse. These new datasets differ substantially in four aspects …
# COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol
We seek to completely revise current models of airborne transmission of respiratory viruses
by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a …
by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a …
Using machine learning to predict the effects and consequences of mutations in proteins
Abstract Machine and deep learning approaches can leverage the increasingly available
massive datasets of protein sequences, structures, and mutational effects to predict variants …
massive datasets of protein sequences, structures, and mutational effects to predict variants …
QMugs, quantum mechanical properties of drug-like molecules
Abstract Machine learning approaches in drug discovery, as well as in other areas of the
chemical sciences, benefit from curated datasets of physical molecular properties. However …
chemical sciences, benefit from curated datasets of physical molecular properties. However …
Δ-Quantum machine-learning for medicinal chemistry
Many molecular design tasks benefit from fast and accurate calculations of quantum-
mechanical (QM) properties. However, the computational cost of QM methods applied to …
mechanical (QM) properties. However, the computational cost of QM methods applied to …