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Molecular Dynamics Simulation

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refineD: improved protein structure refinement using machine learning based restrained relaxation.

Bioinformatics (Oxford, England)
MOTIVATION: Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraint...

Exploring the Scoring Function Space.

Methods in molecular biology (Clifton, N.J.)
In the analysis of protein-ligand interactions, two abstractions have been widely employed to build a systematic approach to analyze these complexes: protein and chemical spaces. The pioneering idea of the protein space dates back to 1970, and the ch...

Machine Learning to Predict Binding Affinity.

Methods in molecular biology (Clifton, N.J.)
Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic ...

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions.

Protein and peptide letters
The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to det...

[An RNA Scoring Function for Tertiary Structure Prediction Based on Multi-layer Neural Networks].

Molekuliarnaia biologiia
A good scoring function is necessary for ab inito prediction of RNA tertiary structures. In this study, we explored the power of a machine learning based approach as a scoring function. Compared with the traditional scoring functions, the present app...

Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design.

The Journal of chemical physics
Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories. This technique furnishes explicit and differentiable ex...

Machine learning accelerates MD-based binding pose prediction between ligands and proteins.

Bioinformatics (Oxford, England)
MOTIVATION: Fast and accurate prediction of protein-ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose predi...

Approaching Pharmacological Space: Events and Components.

Methods in molecular biology (Clifton, N.J.)
With a view to introducing the concept of pharmacological space and its potential applications in investigating and predicting the toxic mechanisms of xenobiotics, this opening chapter describes the logical relations between conformational behavior, ...

Machine Learning and Molecular Dynamics Based Insights into Mode of Actions of Insulin Degrading Enzyme Modulators.

Combinatorial chemistry & high throughput screening
BACKGROUND: Alzheimer's disease (AD) is one of the most common lethal neurodegenerative disorders having impact on the lives of millions of people worldwide. The disease lacks effective treatment options and the unavailability of the drugs to cure th...