AIMC Topic: Biological Products

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Probing the molecular mechanisms of α-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations.

Molecular diversity
Parkinson's disease is characterized by a multifactorial nature that is linked to different pathways. Among them, the abnormal deposition and accumulation of α-synuclein fibrils is considered a neuropathological hallmark of Parkinson's disease. Sever...

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...

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...

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 ...

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...

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...

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...

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...