Benefiting from the highly effective descriptors and knowledge-based analysis of organic reactions, Reaxtica outperformed most published methods for the site-/regioselectivity, enantioselectivity and yield prediction tasks. Furthermore, accurate and fast predictions can be made on new types of reactions as long as there are suitable datasets. Although Reaxtica outperforms other DL-based methods in predicting Suzuki-Miyaura reaction yields for the real-world (USPTO and Reaxys) dataset, further improvements are necessary to get satisfying results. Standardization of reaction data may be necessary for yield prediction task as manually collected experimental data from literatures or patents may contain many different conditions, such as reaction time, temperature, different operation workflow, or experimental equipment. Meanwhile, highly biased dataset like Reaxys that mostly reports successful reactions may not be suitable for yield prediction, consistent with the results of Glorius et al.'s recent work, and an open reaction database that contains data extracted from daily experiments is essential for the community.