AIMC Topic: High-Throughput Screening Assays

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Deep-learning image analysis for high-throughput screening of opsono-phagocytosis-promoting monoclonal antibodies against Neisseria gonorrhoeae.

Scientific reports
Antimicrobial resistance (AMR) is nowadays a global health concern as bacterial pathogens are increasingly developing resistance to antibiotics. Monoclonal antibodies (mAbs) represent a powerful tool for addressing AMR thanks to their high specificit...

Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors.

Journal of biomolecular structure & dynamics
Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are sear...

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...

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...

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...

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...

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...

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...