AIMC Topic: High-Throughput Screening Assays
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High-content screening (HCS) workflows for FAIR image data management with OMERO.
Scientific reports
May 9, 2025
High-content screening (HCS) for bioimaging is a powerful approach to studying biological processes, enabling the acquisition of large amounts of images from biological samples. However, it generates massive amounts of metadata, making HCS experiment...
EDITORIAL: Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning.
Journal of chemical information and modeling
Aug 12, 2024
CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.
Bioinformatics (Oxford, England)
Jun 28, 2024
MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate dr...
[Advancements in virtual screening techniques for study of enzyme inhibitor compounds].
Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica
Dec 1, 2023
Enzymes are closely associated with the onset and progression of numerous diseases, making enzymes a primary target in innovative drug development. However, the challenge remains in identifying compounds that exhibit potent inhibitory effects on the ...
Machine Learning and Artificial Intelligence in Toxicological Sciences.
Toxicological sciences : an official journal of the Society of Toxicology
Aug 25, 2022
Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different ...
Machine learned calibrations to high-throughput molecular excited state calculations.
The Journal of chemical physics
Apr 7, 2022
Understanding the excited state properties of molecules provides insight into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to inc...
Comparative analysis of molecular fingerprints in prediction of drug combination effects.
Briefings in bioinformatics
Nov 5, 2021
Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computa...
Deep inverse reinforcement learning for structural evolution of small molecules.
Briefings in bioinformatics
Jul 20, 2021
The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and high-throughput screening usually make the p...
An ultrafast and flexible liquid chromatography/tandem mass spectrometry system paves the way for machine learning driven in vivo sample processing in early drug discovery.
Rapid communications in mass spectrometry : RCM
Jun 30, 2021
RATIONALE: The low speed and low flexibility of most liquid chromatography/tandem mass spectrometry (LC/MS/MS) approaches in early drug discovery delay sample analysis from routine in vivo studies within the same day. A high-throughput platform for t...
High-throughput developability assays enable library-scale identification of producible protein scaffold variants.
Proceedings of the National Academy of Sciences of the United States of America
Jun 8, 2021
Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in na...
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