Intermediate knowledge enhanced the performance of the amide coupling yield prediction model
Chonghuan Zhang, Qianghua Lin, Chenxi Yang, Yaxian Kong, Zhunzhun Yu, Kuangbiao Liao
Abstract
of 0.71, MAE of 7%, and RMSE of 10%. Meanwhile, the model could recommend suitable conditions for some reactions to elevate the reaction yields. Besides, the model was able to identify which reaction in a reaction pair with a reactivity cliff had a higher yield. In summary, our research demonstrated the feasibility of achieving accurate yield predictions through the combination of HTE and embedding intermediate knowledge into the model. This approach also has the potential to facilitate other related machine learning tasks.
Topics & Concepts
AmideYield (engineering)Key (lock)Coupling (piping)Combinatorial chemistryComputer scienceChemistryComputational chemistryArtificial intelligenceBiochemical engineeringBiological systemEngineeringMaterials scienceOrganic chemistryMechanical engineeringBiologyComputer securityMetallurgyComputational Drug Discovery MethodsInnovative Microfluidic and Catalytic Techniques InnovationChemical Synthesis and Analysis