13 hasil untuk "Daniel Probst"
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Artikel Jurnal
An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification
Abstract Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed alternatives to traditional organic…
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Artikel Jurnal
The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry
While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the scientific community and society as a whole…
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Artikel Jurnal
Visualization of very large high-dimensional data sets as minimum spanning trees
Abstract The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties. However, there are currently no algorithms to visualize such data while preserving both global and local features with a sufficient…
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Artikel Jurnal
A probabilistic molecular fingerprint for big data settings
Abstract Background Among the various molecular fingerprints available to describe small organic molecules, extended connectivity fingerprint, up to four bonds (ECFP4) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately…
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Artikel Jurnal
One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome
Abstract Background Molecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large…
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Artikel Jurnal
Hepatic epithelioid hemangioendothelioma associated with acute disseminated encephalomyelitis by a possible paraneoplastic process
Epithelioid hemangioendothelioma (EHE) is a low-grade, malignant vascular neoplasm that frequently involves the liver, lungs, bone, and soft tissue. Although not commonly associated with a paraneoplastic syndrome, paraneoplastic syndromes in the setting of EHE have been reported. Acute disseminated…
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Artikel Jurnal
Exploring Chemical Space with Machine Learning
Chemical space is a concept to organize molecular diversity by postulating that different molecules occupy different regions of a mathematical space where the position of each molecule is defined by its properties. Our aim is to develop methods to explicitly explore chemical space in the area of…
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Artikel Jurnal
Chemical Space: Big Data Challenge for Molecular Diversity
Chemical space describes all possible molecules as well as multi-dimensional conceptual spaces representing the structural diversity of these molecules. Part of this chemical space is available in public databases ranging from thousands to billions of compounds. Exploiting these databases for drug…
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Artikel Jurnal
Biocatalysed synthesis planning using data-driven learning
As of now, only rule-based systems support retrosynthetic planning using biocatalysis, while initial data-driven approaches are limited to forward predictions. Here, the authors extend the data-driven forward reaction as well as retrosynthetic pathway prediction models based on the Molecular…
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Artikel Jurnal
Enabling Powertrain Technologies for Euro 7/VII Vehicles with Computational Fluid Dynamics
Government regulations on vehicle tailpipe emissions have steadily become more stringent in the last few decades, mainly to reduce gaseous and particulate pollutants from internal combustion engines (ICE) that are deemed harmful for public health and the emissions. With the world experiencing…
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Artikel Jurnal
Fuelling the Digital Chemistry Revolution with Language Models
The RXN for Chemistry project, initiated by IBM Research Europe – Zurich in 2017, aimed to develop a series of digital assets using machine learning techniques to promote the use of data-driven methodologies in synthetic organic chemistry. This research adopts an innovative concept by treating…