118 citations · 164 across the 6 of their papers we have counts for
10 papers
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
Philipp Seidl, Philipp Renz, Natalia Dyubankova +6
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP…
Barking up the right tree: an approach to search over molecule synthesis DAGs
John Bradshaw, Brooks Paige, Matt J. Kusner +2
When designing new molecules with particular properties, it is not only important what to make but crucially how to make it. These instructions form a synthesis directed acyclic gr…
Molecular representation learning with language models and domain-relevant auxiliary tasks
Benedek Fabian, Thomas Edlich, Héléna Gaspar +4
We apply a Transformer architecture, specifically BERT, to learn flexible and high quality molecular representations for drug discovery problems. We study the impact of using diffe…
RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
Cheng-Hao Liu, Maksym Korablyov, Stanisław Jastrzębski +3
De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search process towards more easily synthesizabl…
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces
Marwin H. S. Segler
Some of the most important tasks take place in environments which lack cheap and perfect simulators, thus hampering the application of model-free reinforcement learning (RL). While…
A Model to Search for Synthesizable Molecules
John Bradshaw, Brooks Paige, Matt J. Kusner +2
Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate mole…