Fu, Zunyun; Li, Xutong; Wang, Zhaohui; Li, Zhaojun; Liu, Xiaohong; Wu, Xiaolong; Zhao, Jihui; Ding, Xiaoyu; Wan, Xiaozhe; Zhong, Feisheng; Wang, Dingyan; Luo, Xiaomin; Chen, Kaixian; Liu, Hong; Wang, Jiang; Jiang, Hualiang; Zheng, Mingyue published an article about the compound: Methanesulfonato(2-dicyclohexylphosphino-2′,6′-di-i-propoxy-1,1′-biphenyl)(2′-amino-1,1′-biphenyl-2-yl)palladium(II)( cas:1445085-77-7,SMILESS:O=S(O[Pd]C1=CC=CC=C1C2=C(C=CC=C2)N)(C)=O.CC(C)OC3=CC=CC(OC(C)C)=C3C4=CC=CC=C4P(C5CCCCC5)C6CCCCC6 ).Product Details of 1445085-77-7. Aromatic heterocyclic compounds can be classified according to the number of heteroatoms or the size of the ring. The authors also want to convey more information about this compound (cas:1445085-77-7) through the article.
A feasibility study of a deep learning model for exploring the optimal reaction conditions for given chem. reactions was reported. The model was trained to learn the relationships between the chem. contexts, reaction conditions and product yields based on high-quality existing exptl. data, and then extrapolate reasonably to unseen reactions by in silico exploration of accessible reaction space. This strategy was applied to the Suzuki-Miyaura cross-coupling reaction to find the best catalysts for given reactants and at the same time to discover the optimum combination of the reaction conditions. The trained model able to determine the productive catalysts as well as the most favorable catalyst loading and reaction temperature for both modeled reactions and external unseen reactions was demonstrated. This work aims to provide an insight into the feasibility of introducing a deep learning method in the optimization of chem. reaction conditions.
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Reference:
Isoxazole – Wikipedia,
Isoxazole | C3H3NO – PubChem