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Pharmaceutical Preparations

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A model for predicting drug-disease associations based on dense convolutional attention network.

Mathematical biosciences and engineering : MBE
The development of new drugs is a time-consuming and labor-intensive process. Therefore, researchers use computational methods to explore other therapeutic effects of existing drugs, and drug-disease association prediction is an important branch of i...

Histograms of Frequency-Intensity Distribution Deep Learning to Predict the Seizure Liability of Drugs in Electroencephalography.

Toxicological sciences : an official journal of the Society of Toxicology
Detection of seizures as well as that of seizure auras is effective in improving the predictive accuracy of seizure liability of drugs. Whereas electroencephalography has been known to be effective for the detection of seizure liability, no establish...

Improved prediction of drug-target interactions based on ensemble learning with fuzzy local ternary pattern.

Frontiers in bioscience (Landmark edition)
: The prediction of interacting drug-target pairs plays an essential role in the field of drug repurposing, and drug discovery. Although biotechnology and chemical technology have made extraordinary progress, the process of dose-response experiments ...

Predicting drug-disease associations through layer attention graph convolutional network.

Briefings in bioinformatics
BACKGROUND: Determining drug-disease associations is an integral part in the process of drug development. However, the identification of drug-disease associations through wet experiments is costly and inefficient. Hence, the development of efficient ...

An ultrafast and flexible liquid chromatography/tandem mass spectrometry system paves the way for machine learning driven in vivo sample processing in early drug discovery.

Rapid communications in mass spectrometry : RCM
RATIONALE: The low speed and low flexibility of most liquid chromatography/tandem mass spectrometry (LC/MS/MS) approaches in early drug discovery delay sample analysis from routine in vivo studies within the same day. A high-throughput platform for t...

Patient and Graph Embeddings for Predictive Diagnosis of Drug Iatrogenesis.

Studies in health technology and informatics
In the context of the IA.TROMED project we intend to develop and evaluate original algorithmic methods that will rely on semantic enrichment of embeddings by combining new deep learning algorithms, such as models founded on transformers, and symbolic...

GraphDTA: predicting drug-target binding affinity with graph neural networks.

Bioinformatics (Oxford, England)
SUMMARY: The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to rep...

DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method.

Briefings in bioinformatics
Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce the experimental cost, a large number of computational approaches have been proposed for this task. The machine learning-based model...

DeepPurpose: a deep learning library for drug-target interaction prediction.

Bioinformatics (Oxford, England)
SUMMARY: Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scien...