AIMC Topic: Models, Molecular

Clear Filters Showing 271 to 280 of 759 articles

CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning.

Journal of molecular biology
Recent progress in cryo-EM research has ignited a revolution in biological macromolecule structure determination. Resolution is an essential parameter for quality assessment of a cryo-EM density map, and it is known that resolution varies in differen...

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures.

Annual review of chemical and biomolecular engineering
Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, r...

Real-to-bin conversion for protein residue distances.

Computational biology and chemistry
Protein Structure Prediction (PSP) has achieved significant progress lately. Prediction of inter-residue distances by machine learning and their exploitation during the conformational search is largely among the critical factors behind the progress. ...

Evaluating native-like structures of RNA-protein complexes through the deep learning method.

Nature communications
RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computation...

Integrating Molecular Models Into CryoEM Heterogeneity Analysis Using Scalable High-resolution Deep Gaussian Mixture Models.

Journal of molecular biology
Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning...

Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.

Current opinion in structural biology
Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct a...

Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models.

Journal of chemical information and modeling
Quantitative structure-property relationships (QSPRs) are important tools to facilitate and accelerate the discovery of compounds with desired properties. While many QSPRs have been developed, they are associated with various shortcomings such as a l...

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps.

Journal of molecular biology
The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been th...

3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates.

Molecules (Basel, Switzerland)
Developing molecular generative models for directly generating 3D conformation has recently become a hot research area. Here, an autoencoder based generative model was proposed for molecular conformation generation. A unique feature of our method is ...

Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models.

Methods in enzymology
Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray...