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Theoretical and Computational Chemistry

Working Paper

Multi-Label Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Michael Maser, Alexander Cui, Serim Ryou, Travis DeLano, Yisong Yue, Sarah Reisman

Version 1 posted 15 October 2020

718

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Theoretical and Computational Chemistry

Review

Deep learning for low-data drug discovery: hurdles and opportunities

Derek van Tilborg, Helena Brinkmann, Emanuele Criscuolo, Luke Rossen, Rıza Özçelik, Francesca Grisoni

Version 1 posted 25 January 2024

1,303

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3

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Theoretical and Computational Chemistry

Working Paper

Vibrationally Assisted Tunneling through the Bread of a Proton Sandwich - Connections to Dynamic Matching

Yusef Ahmed, Gabe Gomes, Dean Tantillo

Version 1 posted 22 October 2024

198

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Theoretical and Computational Chemistry

Working Paper

MolAnchor – Explaining Compound Predictions Based on Substructures

Alec Lamens, Jürgen Bajorath

Version 1 posted 08 August 2024

214

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Theoretical and Computational Chemistry

Working Paper

Exposing the limitations of molecular machine learning with activity cliffs.

Derek van Tilborg, Alisa Alenicheva, Francesca Grisoni

Version 3 posted 30 August 2022

2,555

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1

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Theoretical and Computational Chemistry

Working Paper

Interpretable Molecular Property Predictions Using Marginalized Graph Kernels

Yan Xiang, Yu-Hang Tang, Guang Lin, Daniel Reker

Version 1 posted 20 February 2023

448

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Theoretical and Computational Chemistry

Working Paper

Improving Molecular Machine Learning Through Adaptive Subsampling with Active Learning

Yujing Wen, Zhixiong Li, Yan Xiang, Daniel Reker

Version 1 posted 13 February 2023

639

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1

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Refine Search

7 results in Molecular machine learning in Keywords: Molecular machine learning

Multi-Label Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Deep learning for low-data drug discovery: hurdles and opportunities

Vibrationally Assisted Tunneling through the Bread of a Proton Sandwich - Connections to Dynamic Matching

MolAnchor – Explaining Compound Predictions Based on Substructures

Exposing the limitations of molecular machine learning with activity cliffs.

Interpretable Molecular Property Predictions Using Marginalized Graph Kernels

Improving Molecular Machine Learning Through Adaptive Subsampling with Active Learning

7 results in Keywords: Molecular machine learning