Graph attention framework for site-selectivity prediction in undirected C(sp3)–H borylation
- 1Institut für Organische und Biomolekulare Chemie, Wöhler Research Institute for Sustainable Chemistry (WISCh)
- 2Georg-August-Universität Göttingen
Abstract
C–H borylation has surfaced as a powerful transformation in molecular syntheses, providing direct access to organoboron compounds – highly applicable to pharmaceutical and crop protection industries, as well as materials science. While site-selectivity prediction is established for C(sp 2 )–H borylation, significantly more challenging C(sp 3 )–H borylations remain largely unexplored. In sharp contrast, we have developed a graph-attention deep-learning framework combining molecular representations with quantum-mechanical descriptors to predict relative C(sp 3 )–H borylation site selectivities. Using curated literature reactions and a site labeling algorithm, we evaluated various substrate representations and reaction-condition encodings. Bipartite encoding of reaction conditions outperformed independent component encodings. Validation on previously unreported substrates reproduced experimental selectivity distributions, supporting the framework’s robustness and practical utility.
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BibTeX
@misc{pandit2026graph,
title = {{Graph attention framework for site-selectivity prediction in undirected C(sp3)–H borylation}},
author = {Neeraj Kumar Pandit and Max Surke and Parveen Rawal and Lutz Ackermann},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008230/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008230/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Graph attention framework for site-selectivity prediction in undirected C(sp3)–H borylation
AU - Neeraj Kumar Pandit
AU - Max Surke
AU - Parveen Rawal
AU - Lutz Ackermann
PY - 2026
DA - 2026/09/02
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008230/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008230/v1
AB - C–H borylation has surfaced as a powerful transformation in molecular syntheses, providing direct access to organoboron compounds – highly applicable to pharmaceutical and crop protection industries, as well as materials science. While site-selectivity prediction is established for C(sp 2 )–H borylation, significantly more challenging C(sp 3 )–H borylations remain largely unexplored. In sharp contrast, we have developed a graph-attention deep-learning framework combining molecular representations with quantum-mechanical descriptors to predict relative C(sp 3 )–H borylation site selectivities. Using curated literature reactions and a site labeling algorithm, we evaluated various substrate representations and reaction-condition encodings. Bipartite encoding of reaction conditions outperformed independent component encodings. Validation on previously unreported substrates reproduced experimental selectivity distributions, supporting the framework’s robustness and practical utility.
ER -