AIMC Topic: High-Throughput Screening Assays

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Machine Learning for the Discovery, Design, and Engineering of Materials.

Annual review of chemical and biomolecular engineering
Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for th...

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering.

Chemical communications (Cambridge, England)
Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma. Typical enhancements sought in enzyme engineering and evolution campaigns inclu...

Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy.

Pharmaceutical research
OBJECTIVE: Digital microscopy is used to monitor particulates such as protein aggregates within biopharmaceutical products. The images that result encode a wealth of information that is underutilized in pharmaceutical process monitoring. For example,...

Wet-dry-wet drug screen leads to the synthesis of TS1, a novel compound reversing lung fibrosis through inhibition of myofibroblast differentiation.

Cell death & disease
Therapies halting the progression of fibrosis are ineffective and limited. Activated myofibroblasts are emerging as important targets in the progression of fibrotic diseases. Previously, we performed a high-throughput screen on lung fibroblasts and s...

Categorical Matrix Completion With Active Learning for High-Throughput Screening.

IEEE/ACM transactions on computational biology and bioinformatics
The recent advances in wet-lab automation enable high-throughput experiments to be conducted seamlessly. In particular, the exhaustive enumeration of all possible conditions is always involved in high-throughput screening. Nonetheless, such a screeni...

Image-guided MALDI mass spectrometry for high-throughput single-organelle characterization.

Nature methods
Peptidergic dense-core vesicles are involved in packaging and releasing neuropeptides and peptide hormones-critical processes underlying brain, endocrine and exocrine function. Yet, the heterogeneity within these organelles, even for morphologically ...

ECNet is an evolutionary context-integrated deep learning framework for protein engineering.

Nature communications
Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited....

Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities.

eLife
Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-...

MCN-CPI: Multiscale Convolutional Network for Compound-Protein Interaction Prediction.

Biomolecules
In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. Howeve...