Objective

Computer-aided retrosynthesis, also known as computer aided synthesis planning (CASP), is one of the oldest and legendary research topics on the intersection of artificial intelligence and chemistry [1,2]. CASP would be a highly valuable tool to find better synthetic routes and to determine the synthesizability of virtual de-novo designed compounds. However, despite several waves of research, CASP was never widely accepted by chemists, because the systems were slow, and the results were considered to be of unsatisfactory quality [3,4,5]. In this talk, recent findings on retrosynthesis using deep learning and modern search algorithms [6,7] are presented. First, we show that deep neural networks can be trained on very large reaction datasets to predict and rank the most suitable (automatically extracted) transforms to apply to a molecule [6]. This way of training also allows the machine to learn the tolerated and conflicting functional groups of a transform implicitly [6]. In earlier approaches, this information had to be entered manually by experts. Second, to perform search, we employ Monte Carlo Tree Search (MCTS). MCTS allows to efficiently treat problems with very large branching factors, and does not rely strongly on hand-designed search heuristics, which makes it very well suited for retrosynthesis [7]. In comparison to the established search techniques, our approach solves twice as many molecules and is almost two orders of magnitudes faster [7]. Furthermore, we conducted double blind tests to assess the quality of the results. Here, for the first time, organic chemists could not distinguish between real routes taken from the literature and predicted routes [7]. References: [1] G. Vleduts, Information Storage and Retrieval, 1963, 117 [2] E.J. Corey, W.T. Wipke. Science, 1969, 166, 178 [3] W.D. Ihlenfeldt, J. Gasteiger, Angew. Chem. Int. Ed., 1996, 34, 2613 [4] S. Szymkuc et al., Angew. Chem. Int. Ed., 2016, 55, 5904 [5] A. Cook et al., W. Interd. Rev. Comp. Mol. Sci., 2012, 79 [6] M. Segler, M. P. Waller, Chem. Eur. J. 2017, 23, 5966 [7] M. Segler, M. Preuß, M. P. Waller, Nature 2018, 555, 604