Publication ● 31 Aug 2020 ● ACS J. Chem. Inf. Model., 2020Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening In this work, we investigate the feasibility of learning a deep neural network to predict the docking output directly from a two-dimensional compound structure.
Publication ● 27 Jun 2020Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits We propose Molecule Edit Graph Attention Network (MEGAN), a template-free neural model that encodes reaction as a sequence of graph edit.
Our technology is approximately two orders of magnitude faster than most competitors. This enables us to test significantly more approaches and build otherwise unachievable features.
We have achieved state-of-the-art performance on a standard benchmark for retrosynthesis: better than all published approaches, including models used by top competitors.
Our models do not only give accurate predictions, but also explain them to chemists and give relevant estimates of uncertainty. This is crucial for execution of the proposed plans in the laboratory.
Most competing solutions were designed 15+ years ago and do not utilise recent advances in UX design. This is key to achieve wide adoption relatively quickly.
There is no “one-size-fits-all” when it comes to synthesis planning. We offer our clients many customisations, ranging from adapting search criteria to rapid API development for internal integrations.
ble ble ble The “black box” nature of AI makes it challenging to understand why certain predictions are made. Our focus on model interpretability makes us well -positioned to collect more meaningful data faster.
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