Machine learning and retrosynthetic algorithms so far have not provided the desired solution. 50 years after their development classical retrosynthetic methods have not been able to firmly establish themselves in use . Recent proliferation of electronic data and computational resources has seen a rapid growth in the application of machine learning and artificial intelligence to reaction prediction. At present this approach is, however, plagued with sparsity of chemical knowledge and poor data quality , potentially making accurate predictions of reaction outcomes mathematically impossible .