Reaction yield and selectivity such as regioselectivity, site-selectivity, enantioselectivity are among the most critical considerations in organic synthesis. Chemists usually estimate reaction selectivity and yield prior to experiments by experience based on previous knowledge and by searching and analyzing similar reactions in chemical databases or literature. Recently, machine learning (ML) methods have been used to predict reaction selectivity and yield. Quantum mechanics (QM)-based models have been used in many tasks of reaction reactivity prediction. For example, Sigman et al. used density functional theory (DFT) derived descriptors to train multidimensional linear regression models that worked well in predicting organic catalysis, especially in asymmetric catalyst design. Doyle et al. used QM-based descriptors to predict reaction yield and to optimize reaction conditions for high-throughput experimentation (HTE) with Random Forest (RF) models. Similar strategy have been applied by Norrby et al. in predicting reactive site of electrophilic aromatic substitution (EAS). Different from the above strategies, Hong et al. proposed to predict the DFT-computed transition state barrier of radical C-H functionalization reactions and achieved remarkable site-selectivity accuracies. Though these methods have shown outstanding performance in site-selectivity, enantioselectivity, and yield predictions, it is rather demanding for experimental chemists to use as user calculated QM-based descriptors are required.