where α, β, γ, δ ∈ R + . During the training of our model we set α = 10 and β, γ, δ = 1. L ∆∆G is the mean square error (MSE) of the RBFE prediction. L ∆G is the MSE of the absolute binding affinity prediction for both inputs. L rotation is the MSE of the latent vectors of two randomly rotated versions of each protein-ligand pair. This component encourages the latent space representation to ignore the rotation of the protein-ligand complex. L consistency is the MSE of the difference between the predicted absolute binding affinities and the pre-dicted RBFE, to ensure that the model is providing consistent predictions. The Default2018 architecture's weights are initialized with the Xavier uniform method and the biases are initialized to zero. The Dense model is initialized with weights and biases learned from its training described in Francoeur et al. . All models are trained using the Adam stochastic gradient descent optimizer with the default parameters (β 1 = 0.9, β 2 = 0.999, = 1×10 -8 ).