AIMC Topic: Antidepressive Agents

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Prediction of pharmacological activities from chemical structures with graph convolutional neural networks.

Scientific reports
Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent res...

Precision psychiatry in clinical practice.

International journal of psychiatry in clinical practice
The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance criteria is an error-prone process. Recently, pharmacogenetic biomarkers entered in prescribing gui...

Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: In order to reduce unsuccessful treatment trials for depression, neuroimaging and genetic information can be considered as biomarkers. Together with machine-learning methods, prediction models have proved to be valuable for baseline predi...

Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reuptake inhibitor (SSRI), which is the first-line depression treatment. Unfortunately, many depressed p...

Predicting rehospitalization within 2 years of initial patient admission for a major depressive episode: a multimodal machine learning approach.

Translational psychiatry
Machine learning methods show promise to translate univariate biomarker findings into clinically useful multivariate decision support systems. At current, works in major depressive disorder have predominantly focused on neuroimaging and clinical pred...

ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder.

Genes
Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to take effect. This limitation may result in increased risks and treatment costs. To address this limitat...

What do patients learn about psychotropic medications on the web? A natural language processing study.

Journal of affective disorders
BACKGROUND: Low rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information a...

Can Machine Learning help us in dealing with treatment resistant depression? A review.

Journal of affective disorders
BACKGROUND: About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomatic even after multiple trials are applied, configuring a state called treatment resistant depression ...

Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.

PloS one
Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity...