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• W3206796875 abstract "Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natural language approaches that model each problem as a SMILES-to-SMILES translation lead to a simple end-to-end formulation, reduce the need for data preprocessing, and enable the use of well-optimized machine translation model architectures. However, SMILES representations are not an efficient representation for capturing information about molecular structures, as evidenced by the success of SMILES augmentation to boost empirical performance. Here, we describe a novel Graph2SMILES model that combines the power of Transformer models for text generation with the permutation invariance of molecular graph encoders that mitigates the need for input data augmentation. As an end-to-end architecture, Graph2SMILES can be used as a drop-in replacement for the Transformer in any task involving molecule(s)-to-molecule(s) transformations. In our encoder, an attention-augmented directed message passing neural network (D-MPNN) captures local chemical environments, and the global attention encoder allows for long-range and intermolecular interactions, enhanced by graph-aware positional embedding. Graph2SMILES improves the top-1 accuracy of the Transformer baselines by $1.7%$ and $1.9%$ for reaction outcome prediction on USPTO_480k and USPTO_STEREO datasets respectively, and by $9.8%$ for one-step retrosynthesis on the USPTO_50k dataset." @default.
• W3206796875 created "2021-10-25" @default.
• W3206796875 creator A5076162644 @default.
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• W3206796875 date "2021-10-18" @default.
• W3206796875 modified "2023-10-18" @default.
• W3206796875 title "Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction" @default.
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• W3206796875 doi "https://doi.org/10.48550/arxiv.2110.09681" @default.
• W3206796875 hasPublicationYear "2021" @default.
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