The connection between molecular structure and toxicity has been well documented and has been subject to excellent QSAR modelling. Thus, it was believed that toxicity prediction was a natural first exploration for the modular framework. For this proof-of-concept, the regression prediction of therapeutic toxicities (LD50) from basic, 2D structural information of a given small molecule drug was attempted. Data was sourced from the Therapeutics Data Commons (TDC), a repository of drug relevant data for both small molecules and biologics providing useful benchmarks for in silico drug design. For this task, the dataset of interest was the Acute Toxicity dataset consisting of 7,358 small molecule pharmaceutics. This dataset had a built-in scaffold split which was used for their benchmarking. A scaffold split partitions the dataset so that the test set has unseen molecules (or unseen scaffolds). This is considered to be a more challenging target than a random split, whereby randomly chosen molecules are assigned to the test set. Specifically, it is believed that there is less data leakage when employing a scaffold split than a random split. For this toxicity prediction task and all subsequent tasks, an unseen molecules test set policy (scaffold split) was employed. TDC leaderboards from time of writing indicate a top model, Oloren ChemEngine, with an accuracy of 0.552 mean absolute error (MAE) (Eq. 1), indicating that on the test set, Oloren ChemEngine predicted, on average, within ±0.552 units of the true experimental value. For reference, a perfect model would have an MAE = 0. Oloren ChemEngine is an open-source flexible system, which removes much of the code writing for task-specific prediction, instead choosing the best molecular, occasionally proprietary, featurization and model for the user's data. Random Forest, Gaussian Process, and Adaboost models, all of which have noted good performance on molecule property prediction, were chosen as baseline models. Superior performance of Oloren ChemEngine (lower MAE) on TDC's test set against these three baseline models was noted (Table ). The model pretrained on CCDC data, Crystal-Tox, was modestly more accurate than Oloren ChemEngine and significantly more accurate that the three baselines with an average MAE of 0.52 over 5 initializations. As the hypothesis was that pretraining from crystal structure data would yield a model with more chemical knowledge than models without pretraining, each model and baseline was challenged to predict the toxicities of non-therapeutic molecules across a greater range of chemical space. To this end, 12 molecules were chosen: 4 benign molecules (water, sucrose, glucose, and monosodium glutamate), with benign defined as an LD50 value greater than 1,500 mg kg -1 , 4 natural toxins (THC, CBD, aconitine, and epibatidine), and 4 illicit substances (MDMA, cocaine, LSD, and heroin) (Figure ). This new test set comprised a different distribution of chemical toxicity, with the mean and mean toxicity (units = log(kg mol -1 )) of 3.05 and 2.79, compared to the training data with mean and median toxicity measurements of 2.53 and 2.36, and the TDC testing data with the nearly identical mean and median to the training data (Figure ). Additionally, the minimum toxicity values were also lower in this new test set than in the training set, -0.70 compared to -0.34. These new molecules were also, on average, structurally less similar to the training data than the TDC's test set (Figure ). In summary, these new molecules represented a greater slice of chemical toxicity space than had been previously trained or tested upon. Unsurprisingly, baseline models' performance upon testing on the aforementioned compounds dropped significantly to 1.54 (Random Forest), 1.86 (Gaussian Process), and 1.73 (Adaboost) MAE. Oloren ChemEngine still outperformed baselines with a mean MAE of 1.46 with Crystal-Tox showing again a modest improvement over all 4 models with an MAE of 1.38 (Table ). Crystal-Tox was most accurate at compounds in the mid-range toxicity scale, with the most benign (water) and most toxic (epibatidine) having far less extreme predicted toxicity values than reality (Table ). Interestingly, both Oloren ChemEngine and Crystal-Tox correctly identified the higher toxicity of aconitine, also known by its common names "wolfsbane" and "monkshood". The high accuracy of Crystal-Tox was primarily due to its better understanding of non-toxic substances, which were often ranked as ~0.5 units more toxic by Oloren ChemEngine's best model (Table ). Crystal-Tox's outperformance over baselines and Oloren ChemEngine models highlighted the promising nature of this framework towards toxicity prediction of pharmaceutical compounds and toxic, non-pharmaceutical compounds.