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Found 10 papers, 4 papers with code
Barking up the right tree: an approach to search over molecule synthesis DAGs
When designing new molecules with particular properties, it is not only important what to make but crucially how to make it.
RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
A natural idea to mitigate this problem is to bias the search process towards more easily synthesizable molecules using a proxy for synthetic accessibility.
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces
We highlight a recent application, and propose a challenge for the community to assess world program-based planning.
A Model to Search for Synthesizable Molecules
Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure.
Generating Molecules via Chemical Reactions
We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules.
GuacaMol: Benchmarking Models for De Novo Molecular Design
De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles.
A Generative Model For Electron Paths
Chemical reactions can be described as the stepwise redistribution of electrons in molecules.
Learning to Plan Chemical Syntheses
We anticipate that our method will accelerate drug and materials discovery by assisting chemists to plan better syntheses faster, and by enabling fully automated robot synthesis.
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target.
Modelling Chemical Reasoning to Predict Reactions
The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations.
