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Molecular Dynamics Simulation

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Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning.

SAR and QSAR in environmental research
BACE1 has been regarded as an essential drug design target for treating Alzheimer's disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (...

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

Journal of chemical information and modeling
MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in t...

Small molecule inhibitors of IL-1R1/IL-1β interaction identified via transfer machine learning QSAR modelling.

International journal of biological macromolecules
The human interleukin-1 receptor I (IL-1R1) is a cytokine receptor recognized by interleukin 1β (IL-1β), among other cytokines. Over activation of IL-1R1 has been implicated in various inflammatory conditions. This research aims to identify small-mol...

AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.

Journal of chemical theory and computation
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-...

Ab initio characterization of protein molecular dynamics with AIBMD.

Nature
Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency. Classical molecular dynamics simulation is fast but lacks chemical accuracy. Quantum chemistry methods...

Physics-Based Machine Learning Trains Hamiltonians and Decodes the Sequence-Conformation Relation in the Disordered Proteome.

Journal of chemical theory and computation
Intrinsically disordered proteins and regions (IDPs) are involved in vital biological processes. To understand the IDP function, often controlled by conformation, we need to find the link between sequence and conformation. We decode this link by inte...

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems.

Scientific reports
Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement b...

Machine-Learning Approach to Identify Potential Dengue Virus Protease Inhibitors: A Computational Perspective.

The journal of physical chemistry. B
The global prevalence of dengue virus (DENV), a widespread flavivirus, has led to varied epidemiological impacts, economic burdens, and health consequences. The alarming increase in infections is exacerbated by the absence of approved antiviral agent...

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...

A Bio-Inspired Magnetic Soft Robotic Fish for Efficient Solar-Energy Driven Water Purification.

Small methods
Solar-driven water evaporation is a promising solution for global water scarcity but is still facing challenges due to its substantial energy requirements. Here, a magnetic soft robotic bionic fish is developed by combining magnetic nanoparticles (Fe...