论文标题
由量子力学指导的分子设计的加强学习
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
论文作者
论文摘要
使用深钢筋学习（RL）自动化分子设计具有加速发现新化合物的希望。现有方法可与分子图一起使用，因此忽略了原子在太空中的位置，这将它们限制在1）产生单个有机分子和2）启发式奖励函数。为了解决这个问题，我们为笛卡尔坐标中的分子设计提供了一种新颖的RL公式，从而扩展了可以构建的分子类别。我们的奖励函数直接基于基本的物理特性，例如能量，我们通过快速量子化学方法近似。为了实现De-Novo分子设计的进展，我们引入了Molgym，这是一个RL环境，其中包括几个具有挑战性的分子设计任务以及基线。在我们的实验中，我们表明我们的代理可以通过在翻译和旋转不变的状态行动空间中工作有效地学习从头开始解决这些任务。
Automating molecular design using deep reinforcement learning (RL) holds the promise of accelerating the discovery of new chemical compounds. Existing approaches work with molecular graphs and thus ignore the location of atoms in space, which restricts them to 1) generating single organic molecules and 2) heuristic reward functions. To address this, we present a novel RL formulation for molecular design in Cartesian coordinates, thereby extending the class of molecules that can be built. Our reward function is directly based on fundamental physical properties such as the energy, which we approximate via fast quantum-chemical methods. To enable progress towards de-novo molecular design, we introduce MolGym, an RL environment comprising several challenging molecular design tasks along with baselines. In our experiments, we show that our agent can efficiently learn to solve these tasks from scratch by working in a translation and rotation invariant state-action space.
