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19 results in Authors: Hongming Chen

Theoretical and Computational Chemistry

Memory-Assisted Reinforcement Learning for Diverse Molecular De Novo Design

Thomas Blaschke, Ola Engkvist, Jürgen Bajorath, Hongming Chen

553

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Theoretical and Computational Chemistry

Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph

Baiqing Li, Hongming Chen

270

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Theoretical and Computational Chemistry

Exploring the GDB-13 Chemical Space Using Deep Generative Models

Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond, Hongming Chen, Ola Engkvist

1,589

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Theoretical and Computational Chemistry

Practical Notes on Building Molecular Graph Generative Models

Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, Esben Jannik Bjerrum

580

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Theoretical and Computational Chemistry

Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

Noé Sturm, Jiangming Sun, Yves Vandriessche, Andreas Mayr, Günter Klambauer, Lars-Anders Carlson, Ola Engkvist, Hongming Chen

1,029

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Theoretical and Computational Chemistry

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold-hopping capability

Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Ola Engkvist, Hongming Chen

552

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Analytical Chemistry

Randomized SMILES Strings Improve the Quality of Molecular Generative Models

Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist

1,570

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Theoretical and Computational Chemistry

Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction

Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen

1,207

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Theoretical and Computational Chemistry

A Deep Learning Based Scaffold Hopping Strategy for the Design of Kinase Inhibitors

Lizhao Hu, Yuyao Yang, Shuangjia Zheng, Jun Xu, Ting Ran, Hongming Chen

298

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Theoretical and Computational Chemistry

Comparison Between SMILES-Based Differential Neural Computer and Recurrent Neural Network Architectures for De Novo Molecule Design

Simon Viet Johansson, Oleksii Prykhodko, Josep Arús-Pous, Ola Engkvist, Hongming Chen

705

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19 results in Hongming Chen in Authors: Hongming Chen

Memory-Assisted Reinforcement Learning for Diverse Molecular De Novo Design

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Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph

View abstract keyboard_arrow_down

Exploring the GDB-13 Chemical Space Using Deep Generative Models

View abstract keyboard_arrow_down

Practical Notes on Building Molecular Graph Generative Models

View abstract keyboard_arrow_down

Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

View abstract keyboard_arrow_down

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold-hopping capability

View abstract keyboard_arrow_down

Randomized SMILES Strings Improve the Quality of Molecular Generative Models

View abstract keyboard_arrow_down

Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction

View abstract keyboard_arrow_down

A Deep Learning Based Scaffold Hopping Strategy for the Design of Kinase Inhibitors

View abstract keyboard_arrow_down

Comparison Between SMILES-Based Differential Neural Computer and Recurrent Neural Network Architectures for De Novo Molecule Design

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19 results in Authors: Hongming Chen