DeepScreen: An Accurate, Rapid, and Anti‐Interference Screening Approach for Nanoformulated Medication by Deep Learning

Abstract

Accuracy of current efficacy judgment methods for nanoformulated drug remains unstable due to the interference of nanocarriers. Herein, DeepScreen, a drug screening system utilizing convolutional neural network based on flow cytomerty single‐cell images, is introduced. Compared to existing experimental approaches, the high‐throughput system has superior precision, rapidity, and anti‐interference, and is cost‐cutting with high accuracy. First, it can resist most disturbances from manual factors of complicated evaluation progress. In addition, class activation maps generated from DeepScreen indicate that it may identify and locate the tiny variation from cell apoptosis and slight changes of cellular period caused by drug or even nanoformulated drug action at very early stages. More importantly, the excellent performance of assessment on two types of nanoformulations and fluorescent drug proves the fine generality and anti‐interference of this novel system. All these privileged performances make DeepScreen a very smart and promising system for drug detection.

Topical Gel of Vitamin A Solid Lipid Nanoparticles: A Hopeful Promise as a Dermal Delivery System

“… 42 This needs the extended experimental interval since 24 hours or longer treatments has been commonly required for experiments. 43 In order to analyze the cell viability capability of Vit A-SLN2, different concentrations of free Vit A, drug free SLN and Vit A-loaded SLN at 0.5-15 µM were incubated with HFF normal fibroblast cell line obtaining from Pastor Institute (Tehran, Iran) for 24 hours ( Figure 8 ). In equal concentrations of 0.5-10 µM, there was no significant reduction in the viability of cells over 24 h hours in the presence of Vit A, blank SLN and Vit A-SLN2 (P > 0.05 ).…”

“…of the absorbance-based experiments with the ability of the measurement of the metabolic activities of the living cells with a frequent utilization as a result of their simple operation 42. This needs the extended experimental interval since 24 hours or longer treatments has been commonly required for experiments 43. In order to analyze the cell viability capability of Vit A-SLN2, different concentrations of free Vit A, drug free SLN and Vit A-loaded SLN at 0.5-15 µM were incubated with HFF normal fibroblast cell line obtaining from Pastor Institute (Tehran, Iran) for 24 hours(Figure 8).…”

Describing chain-like assembly of ethoxygroup-functionalized organic molecules on Au(111) using high-throughput simulations

“…In this article, we present the results of a combined theoretical and experimental study of elongated organic 1,4-bis(phenylethynyl)-2,5-bis(ethoxy)benzene (PEEB) molecules deposited on Au(111), which can also form quasi-interlocked lateral patterns on Au(111), as revealed through investigation by low temperature (STM). In order to investigate whether those patterns depend specifically on the molecular structure of PEEB, we scan the space of possible adsorption geometries by high-throughput calculations 5 , 6 using density-functional-based tight-binding plus (DFTB+) 7 . The present study follows the choice of high-symmetry adsorption sites for atoms and diatomic molecules in 8 , but additionally employs the symmetry of the substantially larger molecules to define an irreducible wedge in configuration space and sample only that sector.…”

Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning

“…The design of organic materials apparently is one of the focuses for OPV-related research. Unfortunately, the large size of chemical space, which is recently estimated at on the order of 10 6 molecules, makes rational material search very challenging [11][12][13][14][15]. For instance, the Harvard Clean Energy Project explored the molecular space through basic combination rules from an initial collection of 26 molecular fragments and resulted in calculation of material properties for 3.5 million materials [15].…”

“…Recently, machine learning (ML) has been extensively employed for accelerating the virtual screening of organic materials in various fields, such as organic light-emitting diodes (OLEDs) and OPV devices [12][13][14][15][16][17]. In particular, multilayer perceptrons have been used to yield highly accurate predictions on many properties to accelerate material discovery for OPV devices [15].…”