Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model
biomolecular structures such as proteins and their interactions with drug-like small …

Molecular representation learning is the first yet vital step in combining deep learning and
molecular science. To push the boundaries of molecular representation learning, we present …

Identifying conductive metal–organic frameworks (MOFs) with a coupled ion-electron
behavior from a vast array of existing MOFs offers a cost-effective strategy to tap into their …

Traditional methods for identifying “hit” molecules from a large collection of potential drug-
like candidates rely on biophysical theory to compute approximations to the Gibbs free …

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the
coronavirus disease 2019 (COVID-19) pandemic. Several variants of SARS-CoV-2 have …

Protein–ligand interactions are essential to drug discovery and drug development efforts.
Desirable on-target or multitarget interactions are the first step in finding an effective …

Composite science workflows are gaining traction to manage the combined effects of (1)
extreme hardware heterogeneity in new High Performance Computing (HPC) systems and …

In the intersection of molecular science and deep learning, tasks like virtual screening have
driven the need for a high-throughput molecular representation generator on large chemical …

Neural Network (NN) models provide potential to speed up the drug discovery process and
reduce its failure rates. The success of NN models requires uncertainty quantification (UQ) …

Molecular representation learning is essential to deep learning for chemistry, where the
molecules are embedded into continuous real-valued vectors as better representations in …