Figure . Systematic evaluation of drug discovery (AstraZeneca, PubChem) datasets in the inductive setting and their learnt trends, and an analysis of dataset correlations. A. Scatter plots of the low-fidelity/high-fidelity correlation measured by Pearson's r for each dataset (x-axis) and the relative MAE decrease computed for the neural embeddings-augmented transductive models with regards to the non-augmented baseline (y-axis), with the regression line for the two variables and 95% confidence intervals for the regression. B. Systematic evaluation of high-fidelity models using sum and neural embeddings in an inductive setting, where the previous train, validation, and test splits from Figure are used for training and testing is performed on compounds that were measured in subsequent HTS stages. We have also observed cases where both sum and neural based augmentations did not provide uplifts or even decreased performance (Supplementary Figure for all the remaining datasets). C. Example of a model evaluated in this inductive setting where we supply low-fidelity labels (Z-Score) ranging from -50 to 50 in 0.5 increments. Models that rely on sum-based low-fidelity predictions learn nonsensical relationships with linearly increasing pIC50 values. As expected, this is alleviated by the hybrid augmentation, where training uses the raw labels. In contrast, neural-based predictions are initially more conservative than the baseline and slowly surpass the non-augmented models in terms of predicted activity. The multi-fidelity drug discovery datasets are named based on the high-fidelity (DR, dose response) and low-fidelity (SD, single dose) datasets. The abbreviations are: AZ, AstraZeneca; AID, assay identifier; DFT, density functional theory; GFN2-xTB, geometry, frequency, noncovalent, eXtended tight binding; pIC50, negative logarithm of the half maximal inhibitory concentration; MAE, mean absolute error; R 2 , coefficient of determination; AUROC, area under the receiver operating characteristic; MCC, Matthews correlation coefficient. Source data are provided as a Source Data file.