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。