We chose to parameterize s as a standard feedforward neural network that uses 2048-bit countbased extended connectivity fingerprints and a list of two-dimensional descriptors computed via RDKit as molecular input features. We train all models using the Adam optimizer with an initial learning rate of 3 × 10 -4 . Additional molecular featurization and architectural details are available in the accompanying code repository to this study.