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Learning Discrete Neural Reaction Class to Improve Retrosynthesis Prediction
Théophile Gaudin · Animesh Garg · Alan Aspuru-Guzik

Computer-aided retrosynthesis accelerate and innovate the process of molecule and material design, allowing the discovery of new pathways and automating part of the overall development process for drugs and materials. Current machine-learning methods applied to retrosynthesis are limited by their lack of control when generating single-step reactions as they rely on sampling or beam search algorithm. In this work, we apply vector quantized representation learning [1] to learn reaction classes along with retrosynthetic predictions. We represent each reaction class with a vector allowing us to condition the retrosynthetic prediction. We show that learning reaction classes increases control as well as generating more diverse predictions than a baseline model. Our results are a significant step forward in the development of multistep retrosynthesis prediction.

Author Information

Théophile Gaudin (University of Toronto & IBM Research Zurich)
Animesh Garg (University of Toronto, Nvidia, Vector Institute)

I am a CIFAR AI Chair Assistant Professor of Computer Science at the University of Toronto, a Faculty Member at the Vector Institute, and Sr. Researcher at Nvidia. My current research focuses on machine learning for perception and control in robotics.

Alan Aspuru-Guzik (University of Toronto)

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