AIMC Topic: Pharmaceutical Preparations

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Artificial Intelligence in Pharmaceutical Field - A Critical Review.

Current drug delivery
Artificial intelligence is an emerging sector in almost all fields. It is not confined only to a particular category and can be used in various fields like research, technology, and health. AI mainly concentrates on how computers analyze data and mim...

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery.

Current drug targets
Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Ma...

Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives.

Current medicinal chemistry
Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming...

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2.

Current medicinal chemistry
BACKGROUND: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the developme...

An omics perspective on drug target discovery platforms.

Briefings in bioinformatics
The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to di...

Rapid prediction of drug inhibition under heat stress: single-photon imaging combined with a convolutional neural network.

Nanoscale
A method of predicting cellular drug inhibition due to heat stress is presented. Black phosphorus nanosheets are used as photothermal agents to induce stress granule formation in tumor cells. The addition of different drugs induces different thermal ...

A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protein-coupled receptors.

The Journal of chemical physics
Determining the drug-target residence time (RT) is of major interest in drug discovery given that this kinetic parameter often represents a better indicator of in vivo drug efficacy than binding affinity. However, obtaining drug-target unbinding rate...

[Rapid screening and determination of fentanyl and its analogues in drugs by liquid chromatography- quadrupole time-of-flight mass spectrometry].

Se pu = Chinese journal of chromatography
A method based on liquid chromatography coupled with high-resolution quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) was developed for the simultaneous screening and determination of fentanyl and its 26 analogs in liquid and solid powder dru...

A multimodal deep learning framework for predicting drug-drug interaction events.

Bioinformatics (Oxford, England)
MOTIVATION: Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies...

Machine Learning Uncovers Food- and Excipient-Drug Interactions.

Cell reports
Inactive ingredients and generally recognized as safe compounds are regarded by the US Food and Drug Administration (FDA) as benign for human consumption within specified dose ranges, but a growing body of research has revealed that many inactive ing...