  Accurate prediction of protein-ligand binding affinities is crucial for drug
development. Recent advances in machine learning show promising results on this
task. However, these methods typically rely heavily on labeled data, which can
be scarce or unreliable, or they rely on assumptions like Boltzmann-distributed
data that may not hold true in practice. Here, we present DualBind, a novel
framework that integrates supervised mean squared error (MSE) with unsupervised
denoising score matching (DSM) to accurately learn the binding energy function.
DualBind not only addresses the limitations of DSM-only models by providing
more accurate absolute affinity predictions but also improves generalizability
and reduces reliance on labeled data compared to MSE-only models. Our
experimental results demonstrate that DualBind excels in predicting binding
affinities and can effectively utilize both labeled and unlabeled data to
enhance performance.
