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In addition to our proprietary work, we also contribute to research articles, host seminars, and attend speaking events. Visit the connect page to enquire about engagement opportunities.

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Top publications

RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design.

We investigate the feasibility of training deep graph neural networks to approximate the outputs of a retrosynthesis planning software, and their use to bias the search result

We proposed Molecule Edit Graph Attention Network (MEGAN), a template free neural model that encodes reaction as a sequence of graph edit.


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