AIMC Topic: High-Throughput Screening Assays

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The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review.

Biosensors
Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the in...

High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography.

Journal of visualized experiments : JoVE
X-ray crystallography is the most commonly employed technique to discern macromolecular structures, but the crucial step of crystallizing a protein into an ordered lattice amenable to diffraction remains challenging. The crystallization of biomolecul...

20 years of crystal hits: progress and promise in ultrahigh-throughput crystallization screening.

Acta crystallographica. Section D, Structural biology
Diffraction-based structural methods contribute a large fraction of the biomolecular structural models available, providing a critical understanding of macromolecular architecture. These methods require crystallization of the target molecule, which r...

GPCRLigNet: rapid screening for GPCR active ligands using machine learning.

Journal of computer-aided molecular design
Molecules with bioactivity towards G protein-coupled receptors represent a subset of the vast space of small drug-like molecules. Here, we compare machine learning models, including dilated graph convolutional networks, that conduct binary classifica...

High throughput screening of mesenchymal stem cell lines using deep learning.

Scientific reports
Mesenchymal stem cells (MSCs) are increasingly used as regenerative therapies for patients in the preclinical and clinical phases of various diseases. However, the main limitations of such therapies include functional heterogeneity and the lack of ap...

DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features.

Journal of chemical information and modeling
Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estim...

Deep-learning analysis of micropattern-based organoids enables high-throughput drug screening of Huntington's disease models.

Cell reports methods
Organoids are carrying the promise of modeling complex disease phenotypes and serving as a powerful basis for unbiased drug screens, potentially offering a more efficient drug-discovery route. However, unsolved technical bottlenecks of reproducibilit...

Nucleoside Triphosphate Hydrolases Assay in Toxoplasm gondii and Neospora caninum for High-Throughput Screening using a Robot Arm.

Journal of visualized experiments : JoVE
Protozoan parasites infect humans and many warm-blooded animals. Toxoplasma gondii, a major protozoan parasite, is commonly found in HIV-positive patients, organ transplant recipients and pregnant women, resulting in the severe health condition, Toxo...

Generalising from conventional pipelines using deep learning in high-throughput screening workflows.

Scientific reports
The study of complex diseases relies on large amounts of data to build models toward precision medicine. Such data acquisition is feasible in the context of high-throughput screening, in which the quality of the results relies on the accuracy of the ...

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond.

Annual review of pharmacology and toxicology
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of sy...