SCROP Transformer model with neural syntax corrector reaches 59% accuracy on retrosynthesis benchmarks, outperforming prior deep learning methods by over 21 points and template-based methods by over 6 points, with 1.7 times higher accuracy on unseen compounds.
In Neural word embedding as implicit matrix factorization, Advances in neural information processing systems, 2014; pp 2177-2185
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Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks
SCROP Transformer model with neural syntax corrector reaches 59% accuracy on retrosynthesis benchmarks, outperforming prior deep learning methods by over 21 points and template-based methods by over 6 points, with 1.7 times higher accuracy on unseen compounds.