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High-Throughput Screening Assays

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High-throughput cell spheroid production and assembly analysis by microfluidics and deep learning.

SLAS technology
3D cell culture models are important tools in translational research but have been out of reach for high-throughput screening due to complexity, requirement of large cell numbers and inadequate standardization. Microfluidics and culture model miniatu...

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...

Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks.

Journal of chemical theory and computation
Finding new enzyme variants with the desired substrate scope requires screening through a large number of potential variants. In a typical enzyme engineering workflow, it is possible to scan a few thousands of variants, and gather several candidates...

Harmonizing across datasets to improve the transferability of drug combination prediction.

Communications biology
Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the respon...

DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening.

Journal of chemical information and modeling
Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address...

Analysis of cortical cell polarity by imaging flow cytometry.

Journal of cellular biochemistry
Metastasis is the main cause of cancer-related death and therapies specifically targeting metastasis are highly needed. Cortical cell polarity (CCP) is a prometastatic property of circulating tumor cells affecting their ability to exit blood vessels ...

Artificial intelligence-accelerated high-throughput screening of antibiotic combinations on a microfluidic combinatorial droplet system.

Lab on a chip
Microfluidic platforms have been employed as an effective tool for drug screening and exhibit the advantages of lower reagent consumption, higher throughput and a higher degree of automation. Despite the great advancement, it remains challenging to s...

Virtual plates: Getting the best out of high content screens.

SLAS discovery : advancing life sciences R & D
High content screening (HCS) is becoming widely adopted as a high throughput screening modality, using hundred-of-thousands compounds library. The use of machine learning and artificial intelligence in image analysis is amplifying this trend. Another...

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning.

Nature chemistry
Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functio...