论文标题
通过增强学习设计潜在的Covid-19治疗性
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning
论文作者
论文摘要
SARS-COV-2大流行创造了一种治愈的全球种族。一种方法着重于设计人类血管紧张素转换酶2（ACE2）的新型变体，该酶2（ACE2）更紧密地与SARS-COV-2尖峰蛋白结合并将其从人类细胞中转移。在这里，我们将新型的蛋白质设计框架作为增强学习问题。我们通过快速，生物学的奖励函数和顺序的动作空间配方的组合有效地生成新设计。与标准方法相比，政策梯度的使用减少了达到一致的高质量设计所需的计算预算至少一个数量级。通过该方法设计的复合物已通过分子动力学模拟验证，证实了它们的稳定性提高。这表明，将领先的蛋白质设计方法与现代深层增强学习相结合是发现COVID-19治疗的可行途径，并可能加速基于肽的其他疾病的疗法。
The SARS-CoV-2 pandemic has created a global race for a cure. One approach focuses on designing a novel variant of the human angiotensin-converting enzyme 2 (ACE2) that binds more tightly to the SARS-CoV-2 spike protein and diverts it from human cells. Here we formulate a novel protein design framework as a reinforcement learning problem. We generate new designs efficiently through the combination of a fast, biologically-grounded reward function and sequential action-space formulation. The use of Policy Gradients reduces the compute budget needed to reach consistent, high-quality designs by at least an order of magnitude compared to standard methods. Complexes designed by this method have been validated by molecular dynamics simulations, confirming their increased stability. This suggests that combining leading protein design methods with modern deep reinforcement learning is a viable path for discovering a Covid-19 cure and may accelerate design of peptide-based therapeutics for other diseases.
