AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Biological Products

Showing 41 to 50 of 104 articles

Clear Filters

Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers.

Journal of chemical information and modeling
Malaria is a threatening disease that has claimed many lives and has a high prevalence rate annually. Through the past decade, there have been many studies to uncover effective antimalarial compounds to combat this disease. Alongside chemically synth...

Artificial intelligence and machine learning applications in biopharmaceutical manufacturing.

Trends in biotechnology
Artificial intelligence and machine learning (AI-ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI-ML techniques include the growing global demand for biother...

Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning.

Sensors (Basel, Switzerland)
In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is ...

Machine-learning-based ground sink susceptibility evaluation using underground pipeline data in Korean urban area.

Scientific reports
Ground subsidence caused by natural factors, including groundwater, has been extensively researched. However, there have been few studies on ground sink caused mainly by artifacts, including underground pipelines in urban areas. This paper proposes a...

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

Molecular diversity
To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of S...

Developing a Knowledge Graph for Pharmacokinetic Natural Product-Drug Interactions.

Journal of biomedical informatics
BACKGROUND: Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical or other natural products are co-consumed with pharmaceutical drugs. With the growing use of natural products, the risk for potential NPDIs and consequent adve...

A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database.

Journal of biomolecular structure & dynamics
Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery efforts, and our goal was to validate its potential in predicting pharmacological inhibitors of EGLN1 using a deep learning-based architecture, one of its ...

Sulfur-containing marine natural products as leads for drug discovery and development.

Current opinion in chemical biology
Among the large series of marine natural products (MNPs), sulfur-containing MNPs have emerged as potential therapeutic agents for the treatment of a range of diseases. Herein, we reviewed 95 new sulfur-containing MNPs isolated during the period betwe...

Artifical intelligence: a virtual chemist for natural product drug discovery.

Journal of biomolecular structure & dynamics
Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to...

Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs.

PLoS computational biology
Cycling of biologic or targeted synthetic disease modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA) patients due to non-response is a problem preventing and delaying disease control. We aimed to assess and validate treatment res...