Publications by authors named "Aitakin Ezzati"
Mean-field models are a class of models used in computational neuroscience to study the behavior of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behavior of mean-field variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner.
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- Epilepsy is a complex condition that benefits from diverse study methods, including theoretical and computational models.
- The review highlights how dynamical system tools help analyze seizure characteristics and classify them based on their behaviors during onset and offset.
- It emphasizes the potential of computational models for improving clinical practices and personalized medicine, while also considering the role of glial cells and questioning traditional views focused solely on neurons.
Article Synopsis
- - Fragment-based lead discovery is gaining traction in drug development, focusing on identifying weakly binding compounds that can be optimized into high-affinity leads through computational methods like molecular dynamics simulations and free energy perturbation (MD/FEP).
- - The researchers assessed MD/FEP for optimizing fragments binding to the A adenosine receptor, achieving strong correlation with experimental data and showing it outperformed traditional scoring methods in predicting binding affinities.
- - The successful application of MD/FEP in predicting the binding affinities of various compounds indicates that it may become a crucial tool for refining fragment leads into effective drug candidates in the future.