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Theoretical and Computational Chemistry

Working Paper

A Quick Policy to Filter Reactions Based on Feasibility in AI-Guided Retrosynthetic Planning

Samuel Genheden, Ola Engkvist, Esben Jannik Bjerrum

491

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1

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Theoretical and Computational Chemistry

Working Paper

Link-INVENT: Generative Linker Design with Reinforcement Learning

Jeff Guo, Franziska Knuth, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist, Atanas Patronov

2,368

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Theoretical and Computational Chemistry

Working Paper

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Simon Viet Johansson, Hampus Gummesson Svensson, Esben Bjerrum, Alexander Schliep, Morteza Haghir Chehreghani, Christian Tyrchan, Ola Engkvist

470

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1

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Theoretical and Computational Chemistry

Working Paper

REINVENT 2.0 – an AI Tool for De Novo Drug Design

Thomas Blaschke, Josep Arús-Pous, Hongming Chen, Christian Margreitter, Christian Tyrchan, Ola Engkvist, Kostas Papadopoulos, Atanas Patronov

3,334

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Theoretical and Computational Chemistry

Working Paper

Clustering of Synthetic Routes Using Tree Edit Distance

Samuel Genheden, Ola Engkvist, Esben Jannik Bjerrum

307

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1

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Theoretical and Computational Chemistry

Working Paper

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

737

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1

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Theoretical and Computational Chemistry

Working Paper

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

Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen

1,236

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1

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Theoretical and Computational Chemistry

Working Paper

Exploring the GDB-13 Chemical Space Using Deep Generative Models

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

1,619

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Theoretical and Computational Chemistry

Working Paper

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

Dean Sumner, Jiazhen He, Amol Thakkar, Ola Engkvist, Esben Jannik Bjerrum

677

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Theoretical and Computational Chemistry

Working Paper

A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions

Lewis Mervin, Avid M. Afzal, Ola Engkvist, Andreas Bender

888

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42 results in Ola Engkvist in Authors: Ola Engkvist

A Quick Policy to Filter Reactions Based on Feasibility in AI-Guided Retrosynthetic Planning

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Link-INVENT: Generative Linker Design with Reinforcement Learning

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Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

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REINVENT 2.0 – an AI Tool for De Novo Drug Design

View abstract keyboard_arrow_down

Clustering of Synthetic Routes Using Tree Edit Distance

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

View abstract keyboard_arrow_down

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

View abstract keyboard_arrow_down

Exploring the GDB-13 Chemical Space Using Deep Generative Models

View abstract keyboard_arrow_down

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

View abstract keyboard_arrow_down

A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions

View abstract keyboard_arrow_down

42 results in Authors: Ola Engkvist