We identified a series of molecular property prediction tasks with associated datasets reported in the recent literature, ranging from solubility to metabolic susceptibility to reactivity, on which to assess the performance of O-GNN relative to the literature-reported model. We provide an overview of the selected datasets, baseline accuracy and our new results in Table of the results section. In the first task, ESOL, the model was asked to predict the aqueous solubility of a series of small molecules. The ESOL dataset is composed of 1144 structures paired with experimentally measured aqueous solubilities reported in logarithm-transformed units of mol/L. In the reporting publication, the ESOL dataset was fit using molecular descriptors and linear regression, which identified a high dependence of aqueous solubilities on both the calculated octanol-water partition coefficient logP octanol and the proportion of heavy atoms in aromatic systems. Recently, Zhang and Zhang demonstrated improved accu- racy on this task using molecular descriptors, PaDEL features, with a deep neural network (PaDEL-DNN), and we included their achieved RM SE test of 0.62 as the baseline in Table . The second task, BCF , required to predict a bioconcentration factor for the accumulation of a series of small molecules in fish. The BCF dataset covers 1056 molecules, and includes both molecular structures and bioconcentration factors reported as a the logrithmtransformed ratio between concentration in the organism and in the containing water at steady-state. Zhang and Zhang also applied the PaDEL-DNN method to this task, achieving an RM SE test of 0.67. In the third task, Clint, the model was developed to predict the rate of intrinsic metabolic clearance (Cl int ) of a series of small molecules, an important parameter for toxicokinetic modeling. Dawson et al. assembled experimental measurements of Cl int by hepatic cells and microsome assays from the ChEMBL and ToxCast databases, which were standardized into the unit of µL/min/10 6 cells. While they utilized this dataset to train a classifier, we framed a regression problem for consistency with the remainder of the tasks and trained a random forest model with Mordred descriptors, in order to predict the logrithm-transformed Cl int to serve as the baseline model in Table . The last two tasks, O 3 -react and SO 4 -react, asked the model to predict the reactivity of organic contaminants to two oxidants, O 3 (ozone), and SO depth theoretical analysis of graph machine learning approach may be found in literature. We summarize the architectures of NeuralFP and O-GNN in Figure . Mathematically, for graph machine learning methods, we define the molecular graph G as G = (V, E, R), where