AIMC Topic: High-Throughput Screening Assays
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Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.
Archives of toxicology
Apr 28, 2018
Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are not always clear, and thus, a general in vitro assay for testing pulmonotoxicity applicable to a wide ...
Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence?
Drug discovery today
Mar 22, 2018
AI comes to lead optimization: medicinal chemistry in all disease areas can be accelerated by exploiting our pre-competitive knowledge in an unbiased way.
Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.
Cell chemical biology
Mar 1, 2018
In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, who...
Colony analysis and deep learning uncover 5-hydroxyindole as an inhibitor of gliding motility and iridescence in Cellulophaga lytica.
Microbiology (Reading, England)
Feb 5, 2018
Iridescence is an original type of colouration that is relatively widespread in nature but has been either incompletely described or entirely neglected in prokaryotes. Recently, we reported a brilliant 'pointillistic' iridescence in agar-grown colony...
Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters.
ChemMedChem
Feb 1, 2018
False-positive assay readouts caused by badly behaving compounds-frequent hitters, pan-assay interference compounds (PAINS), aggregators, and others-continue to pose a major challenge to experimental screening. There are only a few in silico methods ...
: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning.
eNeuro
Dec 6, 2017
Synapse formation analyses can be performed by imaging and quantifying fluorescent signals of synaptic markers. Traditionally, these analyses are done using simple or multiple thresholding and segmentation approaches or by labor-intensive manual anal...
Integrating DNA structure switch with branched hairpins for the detection of uracil-DNA glycosylase activity and inhibitor screening.
Talanta
Nov 22, 2017
The detection of uracil-DNA glycosylase (UDG) activity is pivotal for its biochemical studies and the development of drugs for UDG-related diseases. Here, we explored an integrated DNA structure switch for high sensitive detection of UDG activity. Th...
Feature selection method based on support vector machine and shape analysis for high-throughput medical data.
Computers in biology and medicine
Oct 12, 2017
Proteomics data analysis based on the mass-spectrometry technique can provide a powerful tool for early diagnosis of tumors and other diseases. It can be used for exploring the features that reflect the difference between samples from high-throughput...
Machine Learning of Human Pluripotent Stem Cell-Derived Engineered Cardiac Tissue Contractility for Automated Drug Classification.
Stem cell reports
Oct 12, 2017
Accurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC)-deriv...
Digging deep into Golgi phenotypic diversity with unsupervised machine learning.
Molecular biology of the cell
Oct 11, 2017
The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportuni...
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