Publication ● 27 Jun 2020Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits One of the key challenges in automated synthesis planning is to generate diverse and reliable chemical reactions. Many reactions can be naturally represented using graph transformation rules referred broadly to as reaction templates. Using reaction templates enables accurate and interpretable predictions but can suffer from limited coverage of the reaction space. On the other hand, template-free methods can increase the coverage but can be prone to making trivial mistakes and are challenging to interpret. A promising idea for constructing more interpretable template-free models is to model a reaction as a sequence of graph edits of the substrates. We extend this idea to retrosynthesis and scale it up to large datasets. We propose Molecule Edit Graph Attention Network (MEGAN), a template-free neural model that encodes reaction as a sequence of graph edits. We achieve competitive performance on both retrosynthesis and forward synthesis and in particular state-of-the-art top-k accuracy for larger K values. Crucially, the latter shows excellent coverage of the reaction space of our model. In summary, MEGAN brings together the strong elements of template-free and template-based models and can be applied to both retro and forward synthesis tasks.
Publication ● 20 Jun 2020We Should at Least Be Able to Design Molecules That Dock Well Designing compounds with desired properties is a key element of the drug discovery process. However, measuring progress in the field has been challenging due to the lack of realistic retrospective benchmarks, and the large cost of prospective validation. To close this gap, we propose a benchmark based on docking, a popular computational method for assessing molecule binding to a protein. Concretely, the goal is to generate drug-like molecules that are scored highly by SMINA, a popular docking software. We observe that popular graph-based generative models fail to generate molecules with a high docking score when trained using a realistically sized training set. This suggests a limitation of the current incarnation of models for de novo drug design. Finally, we propose a simplified version of the benchmark based on a simpler scoring function, and show that the tested models are able to partially solve it. We release the benchmark as an easy to use package available at this https URL. We hope that our benchmark will serve as a stepping stone towards the goal of automatically generating promising drug candidates.
Publication ● 19 Feb 2020Molecule Attention Transformer Designing a single neural network architecture that performs competitively across a range of molecule property prediction tasks remains largely an open challenge, and its solution may unlock a widespread use of deep learning in the drug discovery industry. To move towards this goal, we propose Molecule Attention Transformer (MAT). Our key innovation is to augment the attention mechanism in Transformer using inter-atomic distances and the molecular graph structure. Experiments show that MAT performs competitively on a diverse set of molecular prediction tasks. Most importantly, with a simple self-supervised pretraining, MAT requires tuning of only a few hyperparameter values to achieve state-of-the-art performance on downstream tasks. Finally, we show that attention weights learned by MAT are interpretable from the chemical point of view.
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