Philipp Seidl, Philipp Renz, Natalia Dyubankova, Paulo Neves, Jonas Verhoeven, Jörg K. Wegner, Marwin Segler, Sepp Hochreiter, and and Günter Klambauer

Schematic representation of the method.

Schematic representation of the approach: the Hopfield layer learns to associate the encoded input molecule, the state pattern ξ, with the memory of encoded templates, the stored patterns X.

Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed, which rely on a single-step model of chemical reactivity. In this study, we introduce a template-based single-step retrosynthesis model based on Modern Hopfield Networks, which learn an encoding of both molecules and reaction templates in order to predict the relevance of templates for a given molecule. The template representation allows generalization across different reactions and significantly improves the performance of template relevance prediction, especially for templates with few or zero training examples. With inference speed up to orders of magnitude faster than baseline methods, we improve or match the state-of-the-art performance for top-k exact match accuracy for k ≥ 3 in the retrosynthesis benchmark USPTO-50k. Code to reproduce the results is available at github.com/ml-jku/mhn-react.

Journal of Chemical Information and Modeling, From Reaction Informatics to Chemical Space, 2022-01-15.

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IARAI Authors
Dr Sepp Hochreiter
Research
Drug Discovery
Keywords
Few-Shot Learning, Hopfield Networks, Retrosynthesis, Zero-Shot Learning

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