Retrosynthesis Planning With Learned Templates: Chemist Acceptance as an Evaluation Endpoint

Authors

  • Solange Dumouchel Centre for Computational Chemistry, Bresse Institute of Applied Artificial Intelligence, Bourg-en-Bresse, France Author
  • Teodoro Cattaneo-Gis Centre for Machine Learning Research, Ogliastra Institute of Applied Artificial Intelligence, Tortoli, Italy Author
  • Bostjan Zemljic Department of Human-Computer Interaction, Notranjska Institute of Applied Artificial Intelligence, Postojna, Slovenia Author

DOI:

https://doi.org/10.5281/zenodo.22661283

Keywords:

Retrosynthesis Planning With Learned Templates; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Retrosynthesis Planning With Learned Templates is an area of active development in applied machine learning, with evidence that
has accumulated unevenly across its component questions. We review the current evidence base for retrosynthesis planning with
learned templates, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has
converged and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We
conclude that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance.

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Castellani R, Ristic C, et al. "Retrosynthesis Planning With Learned Templates: a empirical analysis". Knowl Based Syst 32(2014): 432-443. DOI: 10.1177/08404704241291226

Dumitrescu A, Andric D, et al. "Retrosynthesis Planning With Learned Templates: a ablation study". IEEE Trans Neural Netw Learn Syst 34(2017): 443-455. DOI: 10.1093/medlaw/fwad013

Eriksdottir B, Brankovic E, et al. "Retrosynthesis Planning With Learned Templates: a reproducibility study". Proc KDD 36(2020): 454-467. DOI: 10.2196/89834

Farkas I, Ionescu F, et al. "Retrosynthesis Planning With Learned Templates: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.1007/s10916-024-02104-9

Grabowski P, Petrescu G, et al. "Retrosynthesis Planning With Learned Templates: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.3389/fdgth.2026.1896184

Hendriksen W, Wojcik H, et al. "Retrosynthesis Planning With Learned Templates: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.3390/diagnostics15162117

Ionescu F, Foscarini I, et al. "Retrosynthesis Planning With Learned Templates: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.4103/jrpp.jrpp_20_65

Jankowiak G, Grabowski J, et al. "Retrosynthesis Planning With Learned Templates: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.3389/fdgth.2026.1763884

Kowalczyk N, Novotna K, et al. "Retrosynthesis Planning With Learned Templates: a benchmark study". Expert Syst Appl 8(2014): 520-530. DOI: 10.2196/48297

Lindqvist U, Urbanek L, et al. "Retrosynthesis Planning With Learned Templates: a systematic evaluation". Inf Sci 10(2017): 531-542. DOI: 10.1007/s00330-025-12031-z

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Ostrowski L, Lindqvist O, et al. "Retrosynthesis Planning With Learned Templates: a reproducibility study". ACM Trans Softw Eng Methodol 16(2014): 564-578. DOI: 10.2196/69678

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Szabo J, Jankowiak S, et al. "Retrosynthesis Planning With Learned Templates: a comparative analysis". J Mach Learn Res 24(2014): 608-617. DOI: 10.1371/journal.pone.0305949

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Published

2026-09-03

Issue

Vol. 5 No. 2 (2026): vol 5 issue 2 2026

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Articles

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