In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

The impact of molecular dynamics (MD) simulations in molecular biology and drug discovery
has expanded dramatically in recent years. These simulations capture the behavior of …

Molecular docking is an established in silico structure-based method widely used in drug
discovery. Docking enables the identification of novel compounds of therapeutic interest …

The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …

Rational protein engineering requires a holistic understanding of protein function. Here, we
apply deep learning to unlabeled amino-acid sequences to distill the fundamental features …

In silico tools, such as molecular docking, are widely applied to study interactions and
binding affinity of biological activity of proteins and peptides. However, restricted sampling of …

Coarse-grained models have long been considered indispensable tools in the investigation
of biomolecular dynamics and assembly. However, the process of simulating such models is …

Abstract CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and
widely used molecular simulation program. It has been developed over the last three …

Abstract Machine-learned force fields combine the accuracy of ab initio methods with the
efficiency of conventional force fields. However, current machine-learned force fields …

The COVID-19 pandemic has emphasized the need for novel drug discovery process.
However, the journey from conceptualizing a drug to its eventual implementation in clinical …