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High-Throughput Screening Assays

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Intelligently Applying Artificial Intelligence in Chemoinformatics.

Current topics in medicinal chemistry
The intertwining of chemoinformatics with artificial intelligence (AI) has given a tremendous fillip to the field of drug discovery. With the rapid growth of chemical data from high throughput screening and combinatorial synthesis, AI has become an i...

Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes.

Assay and drug development technologies
Deep convolutional neural networks show outstanding performance in image-based phenotype classification given that all existing phenotypes are presented during the training of the network. However, in real-world high-content screening (HCS) experimen...

Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler.

Methods in molecular biology (Clifton, N.J.)
Robust high-content screening of visual cellular phenotypes has been enabled by automated microscopy and quantitative image analysis. The identification and removal of common image-based aberrations is critical to the screening workflow. Out-of-focus...

CAPi: Computational Model for Apicoplast Inhibitors Prediction Against Plasmodium Parasite.

Current computer-aided drug design
BACKGROUND: Discovery of apicoplast as a drug target offers a new direction in the development of novel anti-malarial compounds, especially against the drug-resistant strains. Drugs such as azithromycin were reported to block the apicoplast developme...

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology.

Oncotarget
Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first applic...

Cheminformatics Based Machine Learning Approaches for Assessing Glycolytic Pathway Antagonists of Mycobacterium tuberculosis.

Combinatorial chemistry & high throughput screening
BACKGROUND: Tuberculosis is the second leading cause of death from an infectious disease worldwide after HIV, thus reasoning the expeditions in antituberculosis research. The rising number of cases of infection by resistant forms of M. tuberculosis h...

A novel machine learning method for cytokine-receptor interaction prediction.

Combinatorial chemistry & high throughput screening
Most essential functions are associated with various protein-protein interactions, particularly the cytokine-receptor interaction. Knowledge of the heterogeneous network of cytokine- receptor interactions provides insights into various human physiolo...

Robotic printing and drug testing of 384-well tumor spheroids.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
A major impediment to anti-cancer drug development is the lack of a reliable and inexpensive tumor model to test the efficacy of candidate compounds. This need has emerged due to the insufficiency of widely-used monolayer cultures to predict drug eff...

Multiplexed TEM Specimen Preparation and Analysis of Plasmonic Nanoparticles.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
We describe a system for rapidly screening hundreds of nanoparticle samples using transmission electron microscopy (TEM). The system uses a liquid handling robot to place up to 96 individual samples onto a single standard TEM grid at separate locatio...