AIMC Topic: High-Throughput Screening Assays
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Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.
PLoS computational biology
Jul 2, 2021
There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) an...
A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications.
Scientific reports
May 7, 2021
Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung canc...
Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.
Scientific reports
Apr 15, 2021
The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcri...
DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 6, 2021
High-throughput screening technologies have provided a large amount of drug sensitivity data for a panel of cancer cell lines and hundreds of compounds. Computational approaches to analyzing these data can benefit anticancer therapeutics by identifyi...
Potential neutralizing antibodies discovered for novel corona virus using machine learning.
Scientific reports
Mar 4, 2021
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding pe...
Machine learning identifies candidates for drug repurposing in Alzheimer's disease.
Nature communications
Feb 15, 2021
Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication i...
Cerebral Cortex
Primary Cell Culture
Prescription Drugs
Nootropic Agents
Drugs, Investigational
Humans
Polypharmacology
Machine Learning
Alzheimer Disease
Severity of Illness Index
Nerve Tissue Proteins
High-Throughput Screening Assays
Gene Expression Profiling
Drug Repositioning
Gene Expression Regulation
Pharmacogenetics
Neurons
Neuroprotective Agents
Image-based profiling for drug discovery: due for a machine-learning upgrade?
Nature reviews. Drug discovery
Dec 22, 2020
Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revea...
Machine learning identifies abnormal Ca transients in human induced pluripotent stem cell-derived cardiomyocytes.
Scientific reports
Oct 12, 2020
Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca transients is crucial for evaluating cardiomyocyte function that requires l...
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...
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...
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