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Central Nervous System Agents

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Determination of new psychoactive substances and other drugs in postmortem blood and urine by UHPLC-MS/MS: method validation and analysis of forensic samples.

Forensic toxicology
PURPOSE: This study aimed to validate a modified QuEChERS method followed by ultra-high performance liquid chromatography-tandem mass spectrometry to determine 79 new psychoactive substances (NPS) and other drugs in blood and urine.

A merged molecular representation deep learning method for blood-brain barrier permeability prediction.

Briefings in bioinformatics
The ability of a compound to permeate across the blood-brain barrier (BBB) is a significant factor for central nervous system drug development. Thus, for speeding up the drug discovery process, it is crucial to perform high-throughput screenings to p...

Leading consumption patterns of psychoactive substances in Colombia: A deep neural network-based clustering-oriented embedding approach.

PloS one
The number of health-related incidents caused using illegal and legal psychoactive substances (PAS) has dramatically increased over two decades worldwide. In Colombia, the use of illicit substances has increased up to 10.3%, while the consumption alc...

Machine Learning Exploration of the Relationship Between Drugs and the Blood-Brain Barrier: Guiding Molecular Modification.

Pharmaceutical research
OBJECTIVE: This study aimed to improve the efficiency of pharmacotherapy for CNS diseases by optimizing the ability of drug molecules to penetrate the Blood-Brain Barrier (BBB).

CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design.

Journal of chemical information and modeling
Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discover...

A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish.

Journal of neuroscience methods
BACKGROUND: Although zebrafish are increasingly utilized in biomedicine for CNS disease modelling and drug discovery, this generates big data necessitating objective, precise and reproducible analyses. The artificial intelligence (AI) applications ha...

A Practical Method for Predicting Compound Brain Concentration-Time Profiles: Combination of PK Modeling and Machine Learning.

Molecular pharmaceutics
Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. ...

Prediction of the Extent of Blood-Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model.

Pharmaceutical research
INTRODUCTION: The unbound brain-to-plasma partition coefficient (K) is an essential parameter for predicting central nervous system (CNS) drug disposition using physiologically-based pharmacokinetic (PBPK) modeling. K values for specific compounds ar...

Molecular Generation for CNS Drug Discovery and Design.

ACS chemical neuroscience
Computational drug design is a rapidly evolving field, especially the latest breakthroughs in generative artificial intelligence (GenAI) to create new compounds. However, the potential of GenAI to address the challenges in designing central nervous s...

GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network.

The AAPS journal
The Blood-Brain Barrier (BBB) is a selective barrier between the Central Nervous System (CNS) and the peripheral system, regulating the distribution of molecules. BBB permeability has been crucial in CNS-targeting drug development, such as glioblasto...