With the advancing capabilities of computational methodologies and resources,
ultra-large-scale virtual screening via molecular docking has emerged as a
prominent strategy for in silico hit discovery. Given the exhaustive nature of
ultra-large-scale virtual screening, active learning methodologies have
garnered attention as a means to mitigate computational cost through iterative
small-scale docking and machine learning model training. While the efficacy of
active learning methodologies has been empirically validated in extant
literature, a critical investigation remains in how surrogate models can
predict docking score without considering three-dimensional structural
features, such as receptor conformation and binding poses. In this paper, we
thus investigate how active learning methodologies effectively predict docking
scores using only 2D structures and under what circumstances they may work
particularly well through benchmark studies encompassing six receptor targets.
Our findings suggest that surrogate models tend to memorize structural patterns
prevalent in high docking scored compounds obtained during acquisition steps.
Despite this tendency, surrogate models demonstrate utility in virtual
screening, as exemplified in the identification of actives from DUD-E dataset
and high docking-scored compounds from EnamineReal library, a significantly
larger set than the initial screening pool. Our comprehensive analysis
underscores the reliability and potential applicability of active learning
methodologies in virtual screening campaigns.

通过基于计算方法和资源的不断进步，超大规模虚拟筛选成为一种重要的策略，而主动学习方法在迭代小规模对接和机器学习模型训练中有效地减少了计算成本。本文通过六个受体靶点的基准研究，探究了在仅考虑二维结构的情况下，主动学习方法如何有效地预测对接分数及其适用条件。研究发现，代理模型往往通过记忆从获得步骤中获取的高对接得分化合物中普遍存在的结构模式，虽然如此，代理模型在虚拟筛选中仍然具有使用价值，比如在 DUD-E 数据集中鉴定活性化合物以及 EnamineReal 库中高对接得分化合物的初步筛选。我们的综合分析强调了主动学习方法在虚拟筛选中的可靠性和潜在应用性。

了解分子对接的主动学习及其应用

Understanding active learning of molecular docking and its applications

We seek to automate the generation of drug-like compounds conditioned to
specific protein pocket targets. Most current methods approximate the
protein-molecule distribution of a finite dataset and, therefore struggle to
generate molecules with significant binding improvement over the training
dataset. We instead frame the pocket-conditioned molecular generation task as
an RL problem and develop TacoGFN, a target conditional Generative Flow Network
model. Our method is explicitly encouraged to generate molecules with desired
properties as opposed to fitting on a pre-existing data distribution. To this
end, we develop transformer-based docking score prediction to speed up docking
score computation and propose TacoGFN to explore molecule space efficiently.
Furthermore, we incorporate several rounds of active learning where generated
samples are queried using a docking oracle to improve the docking score
prediction. This approach allows us to accurately explore as much of the
molecule landscape as we can afford computationally. Empirically, molecules
generated using TacoGFN and its variants significantly outperform all baseline
methods across every property (Docking score, QED, SA, Lipinski), while being
orders of magnitude faster.

我们致力于自动化生成与特定蛋白质口袋靶点相匹配的药物样化合物。我们采用强化学习方法，提出了一种目标条件生成流网络（TacoGFN）模型，通过该模型可以生成具有所需属性的分子。我们还开发了基于 Transformer 的对接评分预测方法，用于加速对接评分计算，并使用几轮主动学习来改善对接评分预测。实验证明，使用 TacoGFN 及其变种生成的分子在每个特性（对接评分，QED，SA，Lipinski）上显著优于所有基准方法，并且速度快了数个数量级。