AIMC Topic: High-Throughput Screening Assays

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Quantifying drug tissue biodistribution by integrating high content screening with deep-learning analysis.

Scientific reports Sep 1, 2020
Quantitatively determining in vivo achievable drug concentrations in targeted organs of animal models and subsequent target engagement confirmation is a challenge to drug discovery and translation due to lack of bioassay technologies that can discrim...
Drug Discovery Deep Learning Image Processing, Computer-Assisted Mice Animals Colon Intestine, Small Carbocyanines Tissue Distribution Immunoconjugates Fluorescent Dyes Cadherins High-Throughput Screening Assays
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Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning.

ACS combinatorial science Aug 17, 2020
In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computat...
Combinatorial Chemistry Techniques Peptide Library Machine Learning Structure-Activity Relationship Peptides Ligands Models, Molecular Protein Binding Molecular Structure High-Throughput Screening Assays Amino Acid Sequence
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Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction.

Frontiers in immunology Aug 5, 2020
Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding ce...
CD4 Antigens Ligands Protein Binding Humans Binding Sites Databases, Protein Machine Learning High-Throughput Screening Assays Histocompatibility Antigens Class II HLA Antigens Protein Array Analysis Antigen Presentation Epitopes, T-Lymphocyte Protein Interaction Domains and Motifs
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Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.

Communications biology Jul 15, 2020
The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flo...
Humans RNA, Ribosomal, 16S Flow Cytometry Supervised Machine Learning Microbiota Neural Networks, Computer Biomass Escherichia coli Biodiversity High-Throughput Screening Assays Water Microbiology
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A fully automated deep learning pipeline for high-throughput colony segmentation and classification.

Biology open Jun 23, 2020
Adenine auxotrophy is a commonly used non-selective genetic marker in yeast research. It allows investigators to easily visualize and quantify various genetic and epigenetic events by simply reading out colony color. However, manual counting of large...
Colony Count, Microbial High-Throughput Screening Assays Yeasts Sensitivity and Specificity Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer Reproducibility of Results
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High-Throughput and Autonomous Grazing Incidence X-ray Diffraction Mapping of Organic Combinatorial Thin-Film Library Driven by Machine Learning.

ACS combinatorial science Jun 17, 2020
High-throughput X-ray diffraction (XRD) is one of the most indispensable techniques to accelerate materials research. However, the conventional XRD analysis with a large beam spot size may not best appropriate in a case for characterizing organic mat...
High-Throughput Screening Assays Machine Learning Combinatorial Chemistry Techniques X-Ray Diffraction Organic Chemicals Semiconductors
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Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling.

Archives of toxicology Jun 12, 2020
Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and regulators. The goal of the current study was to develop an in vitro/in silico method for the rapid ...
Transcription Factor RelA Chemical and Drug Induced Liver Injury Primary Cell Culture Actin Cytoskeleton Proto-Oncogene Mas Hepatocytes High-Throughput Screening Assays Toxicity Tests Microscopy, Fluorescence Cellular Senescence Cell Death Machine Learning Phenotype Risk Assessment Dose-Response Relationship, Drug DNA Damage Humans Image Processing, Computer-Assisted Hep G2 Cells
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Machine learning-assisted enzyme engineering.

Methods in enzymology Jun 12, 2020
Directed evolution and rational design are powerful strategies in protein engineering to tailor enzyme properties to meet the demands in academia and industry. Traditional approaches for enzyme engineering and directed evolution are often experimenta...
Amino Acid Sequence High-Throughput Screening Assays Directed Molecular Evolution Artificial Intelligence Machine Learning Protein Engineering
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Robot technology identifies a Parkinsonian therapeutics repurpose to target stem cells of glioblastoma.

CNS oncology May 28, 2020
Glioblastoma is a heterogeneous lethal disease, regulated by a stem-cell hierarchy and the neurotransmitter microenvironment. The identification of chemotherapies targeting individual cancer stem cells is a clinical need. A robotic workstation was ...
Tumor Cells, Cultured Neurotransmitter Agents Robotics Glioblastoma High-Throughput Screening Assays Antineoplastic Agents Humans Neoplastic Stem Cells Parkinson Disease Brain Neoplasms Drug Repositioning
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Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening.

SLAS discovery : advancing life sciences R & D May 13, 2020
There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for bu...
Phenotype Genomics Unsupervised Machine Learning High-Throughput Screening Assays Genome, Human RNA, Small Interfering Supervised Machine Learning Humans
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