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.

当前药物发现的生成模型主要使用分子对接来评估生成化合物的质量，但这种模型在实践中往往无用，因为即使具有较高对接分数的化合物也未必总是显示实验活性。我们提出了一种多保真度的方法，Multi-Fidelity Bind (MFBind)，以实现准确性和计算成本之间的最佳权衡。MFBind 整合了对接和结合自由能模拟器，使用主动学习训练了一个多保真度深度代理模型。我们的深度代理模型利用预训练技术和线性预测头部高效拟合少量高保真度数据。我们进行了大量实验，并表明 MFBind (1) 在代理模型中胜过其他最先进的单一和多保真度基线，并且 (2) 提高了生成模型的性能，生成了质量更高的化合物。