Deep learning is emerging as an effective tool in drug discovery, with
potential applications in both predictive and generative models. Generative
Flow Networks (GFlowNets/GFNs) are a recently introduced method recognized for
the ability to generate diverse candidates, in particular in small molecule
generation tasks. In this work, we introduce double GFlowNets (DGFNs). Drawing
inspiration from reinforcement learning and Double Deep Q-Learning, we
introduce a target network used to sample trajectories, while updating the main
network with these sampled trajectories. Empirical results confirm that DGFNs
effectively enhance exploration in sparse reward domains and high-dimensional
state spaces, both challenging aspects of de-novo design in drug discovery.

深度学习在药物发现中是一种有效的工具，具有在预测模型和生成模型中的潜在应用。本文介绍了双生成流网络（double GFlowNets，DGFNs），这是一种能够生成多样化候选物分子的方法。通过引入强化学习和双深度 Q 学习的概念，我们在采样轨迹时使用了目标网络，并利用这些采样轨迹更新主网络。实证结果表明，DGFNs 有效地增强了在稀疏奖励领域和高维状态空间中的探索能力，这是药物发现中崭新设计的具有挑战性的方面。

双生成流网络

DGFN: Double Generative Flow Networks

Designing new molecules with a set of predefined properties is a core problem
in modern drug discovery and development. There is a growing need for de-novo
design methods that would address this problem. We present MolecularRNN, the
graph recurrent generative model for molecular structures. Our model generates
diverse realistic molecular graphs after likelihood pretraining on a big
database of molecules. We perform an analysis of our pretrained models on
large-scale generated datasets of 1 million samples. Further, the model is
tuned with policy gradient algorithm, provided a critic that estimates the
reward for the property of interest. We show a significant distribution shift
to the desired range for lipophilicity, drug-likeness, and melting point
outperforming state-of-the-art works. With the use of rejection sampling based
on valency constraints, our model yields 100% validity. Moreover, we show that
invalid molecules provide a rich signal to the model through the use of
structure penalty in our reinforcement learning pipeline.

设计新分子用于解决现代药物发现和研发中的核心问题，MolecularRNN 是一种图形循环生成模型，通过对大量分子进行似然训练，生成出多样化的现实分子图。使用增强学习策略梯度算法，提供评估感兴趣属性奖励的评论家，证明与现有的方法相比，在疏水性、药物，熔点等属性上表现得更好。