AIMC Topic: Central Nervous System

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CANDID-CNS: AI Unlocks Stereochemistry and Beyond Rule of 5 to Predict CNS Penetration of Small Molecules.

Journal of chemical information and modeling
Neuroscience is the most difficult therapeutic area for pharmaceutical drug discovery. The blood-brain barrier (BBB) prevents ∼100% of large molecules and >98% of small molecules from penetrating the central nervous system (CNS). Most small molecule ...

Breaking barriers: Medicinal chemistry strategies and advanced in-silico approaches for overcoming the BBB and enhancing CNS penetration.

European journal of medicinal chemistry
Delivering small molecules to the brain and central nervous system (CNS) is greatly hindered by the restrictive blood-brain barrier (BBB), which selectively permits essential molecules while excluding toxic molecules. This selective permeability feat...

CNSGT: Generative Transformer for De Novo Drug Design Targeting the Central Nervous System.

Journal of chemical information and modeling
The design of novel central nervous system (CNS) drugs presents formidable challenges due to the restrictive nature of the blood-brain barrier, which imposes stringent physicochemical requirements. Recent advances in deep learning, particularly Trans...

Artificial intelligence in central-peripheral interaction organ crosstalk: the future of drug discovery and clinical trials.

Pharmacological research
Drug discovery before the 20th century often focused on single genes, molecules, cells, or organs, failing to capture the complexity of biological systems. The emergence of protein-protein interaction network studies in 2001 marked a turning point an...

Unveiling CNS cell morphology with deep learning: A gateway to anti-inflammatory compound screening.

PloS one
Deciphering the complex relationships between cellular morphology and phenotypic manifestations is crucial for understanding cell behavior, particularly in the context of neuropathological states. Despite its importance, the application of advanced i...

During haptic communication, the central nervous system compensates distinctly for delay and noise.

PLoS computational biology
Physically connected humans have been shown to exploit the exchange of haptic forces and tactile information to improve their performance in joint action tasks. As human interactions are increasingly mediated through robots and networks it is importa...

Integrating large-scale single-cell RNA sequencing in central nervous system disease using self-supervised contrastive learning.

Communications biology
The central nervous system (CNS) comprises a diverse range of brain cell types with distinct functions and gene expression profiles. Although single-cell RNA sequencing (scRNA-seq) provides new insights into the brain cell atlases, integrating large-...

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.

Molecular neurobiology
Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the ...

Inference of network connectivity from temporally binned spike trains.

Journal of neuroscience methods
BACKGROUND: Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, meth...

Neuroimaging Scoring Tools to Differentiate Inflammatory Central Nervous System Small-Vessel Vasculitis: A Need for Artificial Intelligence/Machine Learning?-A Scoping Review.

Tomography (Ann Arbor, Mich.)
Neuroimaging has a key role in identifying small-vessel vasculitis from common diseases it mimics, such as multiple sclerosis. Oftentimes, a multitude of these conditions present similarly, and thus diagnosis is difficult. To date, there is no standa...