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Search · four archives
14 papers · ranked by Valyu relevance
Wojciech Michno, Patrick Wehrli, Srinivas Koutarapu, Christian Marsching + 9 more
Understanding of Alzheimer’s disease (AD) pathophysiology, requires molecular assessment of how key pathological factors, specifically amyloid β (Aβ) plaques, influence the surrounding microenvironment. Here, neuronal lipids are particularly of interest as these are implicated in pathological- and neurodegenerative…
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Carotenoids are naturally occurring biomolecules with potent antioxidant activity that humans must obtain through their diet, as they cannot synthesize them endogenously. These pigments are naturally produced by plants and microbes, including red yeast cells that act as micro-factories, particularly genera such as…
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Decades of extensive research have proved that β-amyloid (Aβ) peptides and their aggregation, inducing oxidative stress in the brain, play a key role in Alzheimer’s disease (AD) development. Moreover, Aβ peptides bind to Cu(II) ions, and the resulting complexes accelerate the aggregation process while promoting the…
Yuanqing Lu, Timur Fazletdinov, Zhiwen Pan, Katrin Wondraczek + 1 more
The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization…
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MALDI MS analysis of liquid biopsy combined with ML enables non-invasive disease screening and monitoring. Presented an open-source R-based workflow covering all steps from raw data preprocessing to predictive model evaluation. The pipeline is customizable, transparent, and validated on clinical plasma samples from…
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Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
Daniela Dolciami, Robert Ziolek, Daniel Davies, Michael Carter + 2 more
Chemical diversity is challenging to describe objectively. Despite this, various notions of chemical diversity are used throughout the medicinal chemistry optimization process in drug discovery. In this work, we show the usefulness of considering exploited vectors during different phases of the drug design process to…
Christopher J. Kingsbury, Mathias O. Senge
We imagine molecules to be perfect, but rigidified units can be designed to bend from their ideal shape, discarding their symmetric elements as they process through vibrations and larger, more permanent distortions. The shape of molecules is either simulated or measured by crystallography and strongly affects chemical…
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Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…
Murat Cihan Sorkun, Dajt Mullaj, J. M. Vianney A. Koelman, Süleyman Er
Visualizing chemical spaces streamlines the analysis of molecular datasets by reducing the information to human perception level, hence it forms an integral piece of molecular engineering, including chemical library design, high-throughput screening, diversity analysis, and outlier detection. We present here ChemPlot…
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The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…
Arun Moorthy, Edward Erisman, Anthony Kearsley, Yuxue Liang + 2 more
Fentanyl analogs are a class of designer drugs that are particularly challenging to unambiguously identify due to the mass spectral and retention time similarities of unique compounds. In this paper, we use agglomerative hierarchical clustering to explore the measurement diversity of fentanyl analogs and better…
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Mass spectrometric analysis of inorganic materials is widely used. However, no major advances were made in this area compared to the significant progress in the analysis of biological materials. This work introduces a novel open-source R workflow that efficiently processes and models isotopic distributions in LDI-TOF…
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Traditional and non-classical machine learning models for solid-state structure prediction have predominantly relied on compositional features (derived from properties of constituent elements) to predict the existence of structure and its properties. However, the lack of structural information can be a source of…