Incorporating Neural Networks into the AMOEBA Polarizable Force Field
Neural network potentials (NNPs) offer significant promise to bridge the gap between the
accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular …
accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular …
Distribution of bound conformations in conformational ensembles for X-ray ligands predicted by the ANI-2X machine learning potential
In this study, we systematically studied the energy distribution of bioactive conformations of
small molecular ligands in their conformational ensembles using ANI-2X, a machine …
small molecular ligands in their conformational ensembles using ANI-2X, a machine …
Synergizing Machine Learning, Conceptual Density Functional Theory, and Biochemistry: No-Code Explainable Predictive Models for Mutagenicity in Aromatic …
AH Diaz, M Duque-Noreña, E Rincón… - Journal of Chemical …, 2024 - ACS Publications
This study synergizes machine learning (ML) with conceptual density functional theory
(CDFT) to develop OECD-compliant predictive models for the mutagenic activity of aromatic …
(CDFT) to develop OECD-compliant predictive models for the mutagenic activity of aromatic …
Construction of semiclassical interatomic B–B pair potential to characterize all-boron nanomaterials
L Chkhartishvili - Characterization and Application …, 2023 - systems.enpress-publisher.com
The semiclassical boron–boron interatomic pair potential is constructed in an integral form
allowing its converting into the analytical one. It is an ab initio B–B potential free of any …
allowing its converting into the analytical one. It is an ab initio B–B potential free of any …