AIMC Topic: High-Throughput Screening Assays

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Machine learning in chemoinformatics and drug discovery.

Drug discovery today
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has be...

Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s.

Biomolecules
When excessively activated, C1 is insufficiently regulated, which results in tissue damage. Such tissue damage causes the complement system to become further activated to remove the resulting tissue damage, and a vicious cycle of activation/tissue da...

Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.

Archives of toxicology
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 ...

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

Cell chemical biology
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)
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
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
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
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
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...