AI Medical Compendium Journal:
The Journal of chemical physics

Showing 51 to 60 of 66 articles

Deep learning for variational multiscale molecular modeling.

The Journal of chemical physics
Molecular simulations are widely applied in the study of chemical and bio-physical problems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains chal...

A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protein-coupled receptors.

The Journal of chemical physics
Determining the drug-target residence time (RT) is of major interest in drug discovery given that this kinetic parameter often represents a better indicator of in vivo drug efficacy than binding affinity. However, obtaining drug-target unbinding rate...

Radical scavenging activity of natural antioxidants and drugs: Development of a combined machine learning and quantum chemistry protocol.

The Journal of chemical physics
Many natural substances and drugs are radical scavengers that prevent the oxidative damage to fundamental cell components. This process may occur via different mechanisms, among which, one of the most important, is hydrogen atom transfer. The feasibi...

Artificial neural networks for the inverse design of nanoparticles with preferential nano-bio behaviors.

The Journal of chemical physics
Safe and efficient use of ultrasmall nanoparticles (NPs) in biomedicine requires numerous independent conditions to be met, including colloidal stability, selectivity for proteins and membranes, binding specificity, and low affinity for plasma protei...

Classification of biomass reactions and predictions of reaction energies through machine learning.

The Journal of chemical physics
Elementary steps and intermediate species of linearly structured biomass compounds are studied. Specifically, possible intermediates and elementary reactions of 15 key biomass compounds and 33 small molecules are obtained from a recursive bond-breaki...

evERdock BAI: Machine-learning-guided selection of protein-protein complex structure.

The Journal of chemical physics
Computational techniques for accurate and efficient prediction of protein-protein complex structures are widely used for elucidating protein-protein interactions, which play important roles in biological systems. Recently, it has been reported that s...

Machine learning can be used to distinguish protein families and generate new proteins belonging to those families.

The Journal of chemical physics
Proteins are classified into families based on evolutionary relationships and common structure-function characteristics. Availability of large data sets of gene-derived protein sequences drives this classification. Sequence space is exponentially lar...

Prediction of amyloid aggregation rates by machine learning and feature selection.

The Journal of chemical physics
A novel data-based machine learning algorithm for predicting amyloid aggregation rates is reported in this paper. Based on a highly nonlinear projection from 16 intrinsic features of a protein and 4 extrinsic features of the environment to the protei...

Note: Variational encoding of protein dynamics benefits from maximizing latent autocorrelation.

The Journal of chemical physics
As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the time scale of the latent space while inferring a red...

Blind prediction of protein B-factor and flexibility.

The Journal of chemical physics
The Debye-Waller factor, a measure of X-ray attenuation, can be experimentally observed in protein X-ray crystallography. Previous theoretical models have made strong inroads in the analysis of beta (B)-factors by linearly fitting protein B-factors f...