Artificial intelligence (AI) is revolutionizing the way chemistry research is carried out; in particular, the way drugs and materials are designed and discovered. In fact, in recent years, most large pharmaceutical companies have been adopting AI technologies, and small AI start-ups are burgeoning. However, we believe that successful AI technologies for chemistry would benefit from the synergy between computer science and experiments. Currently, the integration of these two fields is not necessarily happening since (1) models are often trained from structures and data with little domain expert guidance; (2) careful curation of the dataset by domain expert is required and selection of training and testing set is required to ensure reliable evaluation; (3) Experiments are required to generate large, uniform, and high-quality datasets. More specifically, while there are large datasets available for training popular methods (e.g., image recognition) where interpolation is often targeted, thousands of datapoints can hardly represent the chemical space estimated at 10 60 , and extrapolation is required. These models, which are trained using relatively small datasets of molecular structures, or molecular fingerprints are vulnerable to overtraining/memorization or inaccurate correlations and physicochemical properties include subtle effects which require sufficiently large amount of data to cover and quantify them.