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Molecular Dynamics Simulation

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Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles.

Journal of chemical information and modeling
Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates ac...

Deciphering the Complexity of Ligand-Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations.

Journal of chemical information and modeling
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires cl...

Clustering molecular dynamics trajectories for optimizing docking experiments.

Computational intelligence and neuroscience
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the f...

The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning.

Nature communications
The study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to...

Types and effects of protein variations.

Human genetics
Variations in proteins have very large number of diverse effects affecting sequence, structure, stability, interactions, activity, abundance and other properties. Although protein-coding exons cover just over 1 % of the human genome they harbor an di...

Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials.

Physical chemistry chemical physics : PCCP
Investigating the properties of protons in water is essential for understanding many chemical processes in aqueous solution. While important insights can in principle be gained by accurate and well-established methods like ab initio molecular dynamic...

Study on the mechanism of action of the active ingredient of Calculus Bovis in the treatment of sepsis by integrating single-cell sequencing and machine learning.

Medicine
BACKGROUND: Sepsis, a complex inflammatory condition with high mortality rates, lacks effective treatments. This study explores the therapeutic mechanisms of Calculus Bovis in sepsis using network pharmacology and RNA sequencing.

Learning transition path and membrane topological signatures in the folding pathway of bacteriorhodopsin (BR) fragment with artificial intelligence.

The Journal of chemical physics
Membrane protein folding in the viscous microenvironment of a lipid bilayer is an inherently slow process that challenges experiments and computational efforts alike. The folding kinetics is moreover associated with topological modulations of the bio...

Machine Learning-Based Discovery of a Novel Noncovalent MurA Inhibitor as an Antibacterial Agent.

Chemical biology & drug design
The bacterial cell wall is crucial for maintaining the integrity of bacterial cells. UDP-N-acetylglucosamine 1-carboxyethylene transferase (MurA) is an important enzyme involved in bacterial cell wall synthesis. Therefore, it is an important target f...

Leveraging Artificial Intelligence in GPCR Activation Studies: Computational Prediction Methods as Key Drivers of Knowledge.

Methods in molecular biology (Clifton, N.J.)
G protein-coupled receptors (GPCRs) are key molecules involved in cellular signaling and are attractive targets for pharmacological intervention. This chapter is designed to explore the range of algorithms used to predict GPCRs' activation states, wh...