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。

MFBind：一种多保真度方法用于在实际生成建模中评估药物化合物

MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in  Practical Generative Modeling

Machine-learning-based parameterizations (i.e. representation of sub-grid
processes) of global climate models or turbulent simulations have recently been
proposed as a powerful alternative to physical, but empirical, representations,
offering a lower computational cost and higher accuracy. Yet, those approaches
still suffer from a lack of generalization and extrapolation beyond the
training data, which is however critical to projecting climate change or
unobserved regimes of turbulence. Here we show that a multi-fidelity approach,
which integrates datasets of different accuracy and abundance, can provide the
best of both worlds: the capacity to extrapolate leveraging the
physically-based parameterization and a higher accuracy using the
machine-learning-based parameterizations. In an application to climate
modeling, the multi-fidelity framework yields more accurate climate projections
without requiring major increase in computational resources. Our multi-fidelity
randomized prior networks (MF-RPNs) combine physical parameterization data as
low-fidelity and storm-resolving historical run's data as high-fidelity. To
extrapolate beyond the training data, the MF-RPNs are tested on high-fidelity
warming scenarios, $+4K$, data. We show the MF-RPN's capacity to return much
more skillful predictions compared to either low- or high-fidelity (historical
data) simulations trained only on one regime while providing trustworthy
uncertainty quantification across a wide range of scenarios. Our approach paves
the way for the use of machine-learning based methods that can optimally
leverage historical observations or high-fidelity simulations and extrapolate
to unseen regimes such as climate change.

多重保真度方法通过整合不同准确度和丰富度的数据集，结合了基于物理的参数化和基于机器学习的参数化，使得在训练数据范围外进行外推成为可能，且能够提供更高准确性的气候预测模拟，而不需要大量计算资源增加。