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Pharmacology, Clinical

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Scientific and Regulatory Considerations for an Ontogeny Knowledge Base for Pediatric Clinical Pharmacology.

Clinical pharmacology and therapeutics
Understanding all aspects of developmental biology, or pediatric ontogeny, that affect drug therapy from the fetus to the adolescent child is the holy grail of pediatric scientists and clinical pharmacologists. The scientific community is now close t...

Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?

Clinical pharmacology and therapeutics
As the field of artificial intelligence and machine learning (AI/ML) for drug discovery is rapidly advancing, we address the question "What is the impact of recent AI/ML trends in the area of Clinical Pharmacology?" We address difficulties and AI/ML ...

An Introduction to Machine Learning.

Clinical pharmacology and therapeutics
In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithms. However...

TOP: A deep mixture representation learning method for boosting molecular toxicity prediction.

Methods (San Diego, Calif.)
At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a correspond...

The message passing neural networks for chemical property prediction on SMILES.

Methods (San Diego, Calif.)
Drug metabolism is determined by the biochemical and physiological properties of the drug molecule. To improve the performance of a drug property prediction model, it is important to extract complex molecular dynamics from limited data. Recent machin...

Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning.

Methods (San Diego, Calif.)
Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused widespread concern in academia and industry. The existing computational DDI prediction methods are mainly divided into four categories: literature extra...

GCN-BMP: Investigating graph representation learning for DDI prediction task.

Methods (San Diego, Calif.)
One drug's pharmacological activity may be changed unexpectedly, owing to the concurrent administration of another drug. It is likely to cause unexpected drug-drug interactions (DDIs). Several machine learning approaches have been proposed to predict...

Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing.

Therapeutic drug monitoring
BACKGROUND: Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have greatly benefitted from computational and mathematical advances over the past 60 years. Furthermore, the use of artificial intelligence (AI) and machine lea...