  Current generative models for drug discovery primarily use molecular docking
to evaluate the quality of generated compounds. However, such models are often
not useful in practice because even compounds with high docking scores do not
consistently show experimental activity. More accurate methods for activity
prediction exist, such as molecular dynamics based binding free energy
calculations, but they are too computationally expensive to use in a generative
model. We propose a multi-fidelity approach, Multi-Fidelity Bind (MFBind), to
achieve the optimal trade-off between accuracy and computational cost. MFBind
integrates docking and binding free energy simulators to train a multi-fidelity
deep surrogate model with active learning. Our deep surrogate model utilizes a
pretraining technique and linear prediction heads to efficiently fit small
amounts of high-fidelity data. We perform extensive experiments and show that
MFBind (1) outperforms other state-of-the-art single and multi-fidelity
baselines in surrogate modeling, and (2) boosts the performance of generative
models with markedly higher quality compounds.
