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Miriam Mathea

Researcher at Bosch

Publications -  9
Citations -  1274

Miriam Mathea is an academic researcher from Bosch. The author has contributed to research in topics: Graph (abstract data type) & Artificial neural network. The author has an hindex of 7, co-authored 7 publications receiving 516 citations.

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Analyzing Learned Molecular Representations for Property Prediction.

TL;DR: In this article, a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets is presented.
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Analyzing Learned Molecular Representations for Property Prediction

TL;DR: A graph convolutional model is introduced that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets.
Journal ArticleDOI

Key Read Across Framework Components and Biology Based Improvements

TL;DR: Current state of the art criteria for good read across is provided, but also how read-across can be further developed in the near future is indicated.
Journal ArticleDOI

Correction to Analyzing Learned Molecular Representations for Property Prediction.

TL;DR: Property Prediction Kevin Yang,*, Kyle Swanson,*,† Wengong Jin,† Connor Coley,‡ Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola and Regina Barzilay.