AIMC Topic: Molecular Dynamics Simulation
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A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.
Journal of chemical theory and computation
Sep 17, 2024
The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and m...
Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.
Journal of chemical information and modeling
Sep 16, 2024
Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Compet...
Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR.
Nature communications
Sep 16, 2024
Allosteric drugs offer a new avenue for modern drug design. However, the identification of cryptic allosteric sites presents a formidable challenge. Following the allostery nature of residue-driven conformation transition, we propose a state-of-the-a...
Exploring the Global Reaction Coordinate for Retinal Photoisomerization: A Graph Theory-Based Machine Learning Approach.
Journal of chemical information and modeling
Sep 11, 2024
Unraveling the reaction pathway of photoinduced reactions poses a great challenge owing to its complexity. Recently, graph theory-based machine learning combined with nonadiabatic molecular dynamics (NAMD) has been applied to obtain the global reacti...
Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.
International journal of molecular sciences
Sep 8, 2024
Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence...
In silico assessments of the small molecular boron agents to pave the way for artificial intelligence-based boron neutron capture therapy.
European journal of medicinal chemistry
Sep 6, 2024
Boron neutron capture therapy (BNCT) is a highly targeted, selective and effective technique to cure various types of cancers, with less harm to the healthy cells. In principle, BNCT treatment needs to distribute the boron (B) atoms inside the tumor ...
Investigating PCB degradation by indigenous fungal strains isolated from the transformer oil-contaminated site: degradation kinetics, Bayesian network, artificial neural networks, QSAR with DFT, molecular docking, and molecular dynamics simulation.
Environmental science and pollution research international
Sep 6, 2024
The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian network...
Combining network pharmacology, machine learning, molecular docking and molecular dynamic to explore the mechanism of Chufeng Qingpi decoction in treating schistosomiasis.
Frontiers in cellular and infection microbiology
Sep 6, 2024
BACKGROUND: Although the Chufeng Qingpi Decoction (CQD) has demonstrated clinical effectiveness in the treatment of schistosomiasis, the precise active components and the underlying mechanisms of its therapeutic action remain elusive. To achieve a pr...
A Deep Learning Approach to Uncover Voltage-Gated Ion Channels' Intermediate States.
The journal of physical chemistry. B
Aug 30, 2024
Owing to recent advancements in cryo-electron microscopy, voltage-gated ion channels have gained a greater comprehension of their structural characteristics. However, a significant enigma remains unsolved for a large majority of these channels: their...
From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Aug 29, 2024
Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only ta...
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