Tokyo, Japan * As of July, 2022. ** On the USPTO-MIT dataset. [1] (2020, Dai, H. et al.): https://doi.org/10.48550/arXiv.2001.01408. [2] (2020, Tetko, I.V. et al.): https://doi.org/10.1038/s41467-020-19266-y. [3] (2021, Somnath, V.R. et al.): https://doi.org/10.48550/arXiv.2006.07038. [4] (2021, Chen, S. and Jung, Y.): https://doi.org/10.1021/jacsau.1c00246. [5] (2022, Ucak, U.V. et al.): https://doi.org/10.1038/s41467-022-28857-w. [6] (2022, Zhong, Z. et al.): https://doi.org/10.1039/d2sc02763a. [7] (2017, Coley, C.W. et al.): https://doi.org/10.1021/acscentsci.7b00355. [8] (2020, Jin, W. et al.): https://doi.org/10.48550/arXiv.1709.04555. [9] (2021, Kearnes, S.M. et al.): https://doi.org/10.1021/jacs.1c09820. Step 1. Literature Review * Step 2. Dataset Preparation Step 3. Evaluation USPTO-50k [7] USPTO-MIT [8] ORD (Non-USPTO) [9] Top-N Accuracy - Aggregated accuracy that reflects probability of the ground truth being found within the first N suggestions. Other single-step retrosynthesis metrics: 1. MaxFrag Accuracy 2. Suggestion Duplication Rate 3. Suggestion Chemical Validity 4. Round-trip Accuracy 5. Coverage Rate 6. Diversity Rate… Approach Year Type Top-1 (%) GLN [1] 2020 TB 52.5 GraphRetro [3] 2021 Semi-TB 53.7 LocalRetro [4] 2021 TB 53.4 Approach Year Type Top-1 (%) AT [2] 2020 TF 53.5 RetroTRAE [5] 2022 TF 61.6 ** R-SMILES [6] 2022 TF 56.3