CLIFF: A component-based, machine-learned, intermolecular force field
Computation of intermolecular interactions is a challenge in drug discovery because
accurate ab initio techniques are too computationally expensive to be routinely applied to …
accurate ab initio techniques are too computationally expensive to be routinely applied to …
Machine-learning based prediction of small molecule–surface interaction potentials
I Rouse, V Lobaskin - Faraday Discussions, 2023 - pubs.rsc.org
Predicting the adsorption affinity of a small molecule to a target surface is of importance to a
range of fields, from catalysis to drug delivery and human safety, but a complex task to …
range of fields, from catalysis to drug delivery and human safety, but a complex task to …
Intermolecular Non-Bonded Interactions from Machine Learning Datasets
JA Chen, SD Chao - Molecules, 2023 - mdpi.com
Accurate determination of intermolecular non-covalent-bonded or non-bonded interactions
is the key to potentially useful molecular dynamics simulations of polymer systems …
is the key to potentially useful molecular dynamics simulations of polymer systems …
A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets
ZX Fan, SD Chao - Bioengineering, 2024 - mdpi.com
Accurate energy data from noncovalent interactions are essential for constructing force fields
for molecular dynamics simulations of bio-macromolecular systems. There are two important …
for molecular dynamics simulations of bio-macromolecular systems. There are two important …