AIMC Topic: Deep Learning

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Accurate Identification of MDMB-Type Synthetic Cannabinoids through Design of Dual Excited-State Intramolecular Proton Transfer Site Probe and Deep-Learning.

Analytical chemistry
Synthetic cannabinoids, a novel class of highly toxic psychoactive substances with various disguised forms, have posed significant risks to public safety, and their weak reactivity presents a substantial challenge for swift and accurate analysis. In ...

Automated Whole-Brain Focal Cortical Dysplasia Detection Using MR Fingerprinting With Deep Learning.

Neurology
BACKGROUND AND OBJECTIVES: Focal cortical dysplasia (FCD) is a common pathology for pharmacoresistant focal epilepsy, yet detection of FCD on clinical MRI is challenging. Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging techniq...

Enhancing YOLOv8n with Mamba-like linear attention for defect detection and coating thickness analysis of irregular film tablet.

International journal of pharmaceutics
This study presents a real-time system that integrates deep learning and machine vision for defect detection and coating thickness measurement of irregularly shaped film-coated tablets. To overcome the accuracy and speed limitations of the traditiona...

Deep learning-based image classification and quantification models for tablet sticking.

International journal of pharmaceutics
Sticking can significantly affect drug product quality, manufacturing efficiency, and therapeutic efficacy in pharmaceutical tablet manufacturing. This study presents a novel integrated model with a convolutional neural network (CNN) and gray-level c...

Decoding the Structure-Activity Relationship of the Dopamine D3 Receptor-Selective Ligands Using Machine and Deep Learning Approaches.

Journal of chemical information and modeling
Dysfunctions of the dopamine D2 and D3 receptors (D2 and D3) are implicated in neuropsychiatric conditions such as Parkinson's disease, schizophrenia, and substance use disorders (SUDs). Evidence indicates that D3-selective ligands can effectively mo...

Femtosecond Laser Treatment of Ti Surfaces: Antibacterial Mechanisms and Deep Learning-Based Surface Recognition.

ACS biomaterials science & engineering
Bacterial infections have been demonstrated to cause the premature failure of implants. A reliable strategy for preserving biocompatibility is to physically modify the implant surface, without using chemicals, to prevent bacterial adhesion. This stud...

GICL: A Cross-Modal Drug Property Prediction Framework Based on Knowledge Enhancement of Large Language Models.

Journal of chemical information and modeling
Deep learning models have demonstrated their potential in learning effective molecular representations critical for drug property prediction and drug discovery. Despite significant advancements in leveraging multimodal drug molecule semantics, existi...

Deep-Learning-Based Integration of Sequence and Structure Information for Efficiently Predicting miRNA-Drug Associations.

Journal of chemical information and modeling
Extensive research has shown that microRNAs (miRNAs) play a crucial role in cancer progression, treatment, and drug resistance. They have been recognized as promising potential therapeutic targets for overcoming drug resistance in cancer treatment. H...

To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry.

Journal of proteome research
Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data c...

Unsupervised Adaptive Deep Learning Framework for Video Denoising in Light Scattering Imaging.

Analytical chemistry
Light scattering is a powerful tool that has been widely applied in various scenarios, such as nanoparticle analysis, single-cell measurement, and blood flow monitoring. However, noise is always a concerning and challenging issue in light scattering ...